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<p><h4>Mindrift is looking for highly skilled Python data scraping engineers to join the Tendem project and drive specialized data scraping workflows within our hybrid AI + human system.</h4> <p>In this role, as an AI pilot – that’s how we refer to this role at Mindrift – you’ll collaborate with Tendem agents that handle repetitive tasks, while you provide critical thinking, domain expertise, and quality control to deliver accurate and actionable results.</p> <p>This part-time remote opportunity is ideal for technical professionals with hands-on experience in web scraping, data extraction, and processing.</p> <h4>What we do</h4> <p>The Mindrift platform connects specialists with AI projects from major tech innovators. Our mission is to unlock the potential of generative AI by tapping into real-world expertise from across the globe.</p> <h4>About the role</h4> <p>This is a freelance role for a Tendem project. As a Python data scraping engineer, you'll handle data scraping tasks requiring technical precision for web extraction and processing, utilizing various tools such as our provided Apify and OpenRouter alongside your own resourceful approaches.</p> <h4>Key responsibilities</h4> <ul> <li>Own end-to-end data extraction workflows across complex websites, ensuring complete coverage, accuracy, and reliable delivery of structured datasets.</li> <li>Leverage internal tools (Apify, OpenRouter) alongside custom workflows to accelerate data collection, validation, and task execution while meeting defined requirements.</li> <li>Ensure reliable extraction from dynamic and interactive web sources, adapting approaches as needed to handle JavaScript-rendered content and changing site behavior.</li> <li>Enforce data quality standards through validation checks, cross-source consistency controls, adherence to formatting specifications, and systematic verification prior to delivery.</li> <li>Scale scraping operations for large datasets using efficient batching or parallelization, monitor failures, and maintain stability against minor site structure changes.</li> </ul> <h4>Compensation</h4> <p>On this project, contributors can earn up to $37 per hour equivalent, depending on their level and pace of contribution.</p> <p>Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.</p> <h4>How to get started</h4> <p>Simply apply to this post, qualify, and get the chance to contribute to projects that match your technical skills, on your own schedule. From coding and automation to fine-tuning AI outputs, you’ll play a key role in advancing AI capabilities and real-world applications.</p> <h4>Requirements</h4> <ul> <li>At least 3 years of relevant experience in data engineering, web scraping, automation, or software development (required).</li> <li>Bachelor's or Master’s degree in engineering, applied mathematics, computer science, or related technical fields is a plus.</li> <li>Strong experience in Python web scraping (BeautifulSoup, Selenium or similar), including dynamic content (JS, AJAX, infinite scroll) and APIs via proxies.</li> <li>Proven ability to extract data from complex structures (hierarchies, archived pages, inconsistent HTML).</li> <li>Solid background in data cleaning, normalization, and validation, delivering structured datasets (CSV, JSON, Google Sheets).</li> <li>Hands-on experience with LLMs and AI frameworks to enhance automation and problem-solving.</li> <li>Strong attention to detail and commitment to data accuracy.</li> <li>Self-directed work ethic with ability to troubleshoot independently.</li> <li>English proficiency: Upper-intermediate (B2) or above (required).</li> </ul> <h4>Benefits</h4> <p>Why this freelance opportunity might be a great fit for you?</p> <ul> <li>Work fully remote on your own schedule with just a laptop and stable internet connection.</li> <li>Gain hands-on experience in a unique hybrid environment where human expertise and AI agents collaborate seamlessly — a distinctive skill set in a rapidly growing field.</li> <li>Participate in performance-based bonus programs that reward high-quality work and consistent delivery.</li> </ul></p><p></p>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>A leading global pharmaceutical company is seeking a Director, Data &amp; Analytics to take ownership of the strategy, architecture, delivery, and operational performance of its Data &amp; Analytics function. This is a hands-on leadership role overseeing the enterprise data platform, the data products the business relies on, and the team behind them, setting technical direction, delivery standards, and investment priorities while staying personally engaged in key architecture and design decisions. The ideal candidate combines strong technical credibility with senior leadership experience, and will be directly accountable for making the platform AI-ready, ensuring data is governed, traceable, and model-grade to support regulated AI deployment.</p><p>Responsibilities:</p><p>1. Platform Architecture &amp; Engineering Leadership:</p><ul><li>Own the enterprise data platform, Azure Data Lake Storage Gen2, Databricks lakehouse (Medallion architecture), Power BI, and Unity Catalog, ensuring it is architecturally sound, standardised, reliable, and engineered to scale</li><li>Set and enforce platform engineering standards: ingestion patterns, transformation conventions, Medallion layer contracts, data quality gates, Bronze-to-Gold promotion criteria, and the CI/CD framework that delivers all of it</li><li>Own the Unity Catalog governance model, RBAC, lineage, business glossary, and metric definitions, as the platform-enforced foundation for trusted data</li><li>Drive the platform roadmap from current state to target, sequencing technical debt remediation, new capability build-out, and platform readiness for downstream AI and analytics demand</li><li>Personally lead design reviews and architecture decisions for the platform, engaging directly with the engineering team on complex technical problems where senior technical judgment is required</li><li>Own data platform observability and operational excellence, pipeline reliability, SLA adherence, incident response, and data quality monitoring</li></ul><p>2. Delivery &amp; Data Products:</p><ul><li>Run the D&amp;A delivery programme, from source ingestion and pipeline engineering through to analytics, semantic layer, and data product delivery for business functions</li><li>Set and enforce delivery standards: sprint cadence, code review, documentation, testing, validation, and release management practices that govern all team output</li><li>Define and own data SLAs to the business, pipeline refresh frequency, availability, and incident response commitments, and ensure delivery is held against them</li><li>Own the data integration roadmap, prioritising ingestion of new source systems into the lake in alignment with business demand and platform readiness</li><li>Manage complex integration challenges across source systems, engaging directly on technical constraints, working with upstream owners, and designing solutions that align data latency and refresh frequency with business needs</li><li>Own the portfolio of data products, datasets, semantic models, and analytics deliverables, ensuring each is documented, well-understood, and fit for the business question it answers</li><li>Define and own the standard for what a Gold-layer data product must satisfy to be AI-ready, distinguishing analytics-grade from model-training-grade, with explicit criteria covering completeness, label integrity, statistical consistency, and lineage traceability</li></ul><p>3. Data Governance &amp; Quality:</p><ul><li>Design and operate the data governance operating model, data ownership, stewardship, quality standards, business glossary, and metric definitions</li><li>Embed data quality gates into Medallion layer promotion, making quality a precondition of Bronze-to-Gold progression, with measurable thresholds and clear remediation paths</li><li>Own data lineage visibility across the platform so business users can trace the provenance of every number they rely on, end-to-end from source to dashboard</li><li>Lead the technical evaluation, selection, and implementation of enterprise data catalogue tooling, including integration with Unity Catalog and the platform metadata layer</li><li>Embed appropriate data handling, validation, and audit-trail practices for regulated data domains, partnering with Quality, Regulatory, and Legal to ensure compliance requirements are met by design</li><li>Establish data quality measurement as a managed practice, KPIs, dashboards, periodic review, and accountability with data owners</li><li>Establish data lineage and provenance practices that satisfy AI explainability and regulatory auditability requirements, including GxP-compliant audit trails for Quality, Manufacturing, and Regulatory AI use cases</li></ul><p>4. Team Leadership &amp; Capability Development:</p><ul><li>Lead, hire, and develop the D&amp;A team, Data Engineers, Analytics Engineers, BI Developers, and Business Analysts, sequencing hires in alignment with the platform roadmap and delivery demand</li><li>Define and evolve the role design, skills profile, and career framework for the team within the broader Data &amp; AI CoE structure</li><li>Set the team's performance culture: clear ownership, high engineering standards, fast feedback, continuous learning, and documentation as a team discipline</li><li>Coach and develop team members directly, identifying high-potential individuals, investing in their technical and leadership growth, and building bench strength across roles</li><li>Manage team capacity and allocation across the platform roadmap and business delivery demand, ensuring focus stays on high-value work aligned to strategic priorities</li><li>Set the engineering craft culture, code review, design review, pairing, and shared technical standards, that lifts the technical quality of all team output</li></ul></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><p>Bachelor's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or related discipline</p><p>Master's degree in Data Science, Computer Science, Business Administration, or related field is preferred</p><p>Relevant certifications in cloud data platforms (e.g., Azure Data Engineer, Databricks Certified Data Engineer Professional) are preferred</p><p>Minimum 10 years of progressive experience in data engineering, data platform, or enterprise analytics roles</p><p>Minimum 5 years in a senior leadership role with direct team management accountability, hiring, developing, and performance managing a multi-disciplinary technical team</p><p>Proven hands-on production experience with Databricks (Delta Lake, Unity Catalog) and Azure data services at platform-design and engineering-lead level</p><p>Proven track record building or substantially remediating a cloud-native data platform in a complex, multi-source enterprise environment</p><p>Experience with Medallion/lakehouse architecture patterns in production</p><p>Experience designing data products and platform capabilities for AI/ML consumption, including Feature Store design, training dataset engineering, and ML data lineage is preferred</p><p>Experience working at the interface of a data platform team and an AI/ML team, translating model requirements into data infrastructure specifications and owning the data readiness handoff is preferred</p><p>Experience leading a data governance or catalogue implementation, from design through to business adoption</p><p>Life sciences, pharmaceutical, or other regulated industry experience is preferred</p><p>Skills :</p><p>Technical Competencies:</p><ul><li>Data Platform Architecture</li><li>Data Engineering, Pipeline Design &amp; CDC Patterns</li><li>Data Modelling, Transformation &amp; Engineering Standards</li><li>Data Governance, Quality &amp; Catalogue</li><li>Analytics &amp; BI Delivery</li><li>MLOps &amp; ML Platform Foundations</li><li>Delivery Management &amp; Agile Methods</li></ul><p>Platform &amp; Technical Skills:</p><ul><li>Deep practical expertise in Azure data services: ADLS Gen2, Azure Data Factory, Azure DevOps</li><li>Hands-on production experience with Databricks, Delta Lake, notebooks, Unity Catalog, MLflow</li><li>Strong understanding of incremental load and CDC patterns across enterprise source systems (SAP, Veeva, SuccessFactors, and similar)</li><li>Power BI at the semantic layer level, understanding how the semantic layer should be designed for enterprise scale</li><li>CI/CD for data pipelines, practical implementation at production scale</li><li>Data quality frameworks, profiling, expectation testing, alerting, and remediation workflows</li><li>Working knowledge of modern data governance tooling: Unity Catalog, Collibra, Purview, or DataHub</li></ul><p>Leadership &amp; Business Skills:</p><ul><li>Credible with both technical teams and senior business stakeholders</li><li>Strong delivery discipline, owns commitments, communicates risks early, and sizes work realistically</li><li>Structured thinker, able to take a complex current state and produce a clear, prioritised, sequenced roadmap</li><li>Strong written and verbal communication in English; Arabic proficiency valued</li><li>Cultural intelligence for working effectively across MENA, US, and Europe</li></ul><p></p></section>

<p><h4>Description</h4> <p>A leading global pharmaceutical company is seeking a Director, Data & Analytics to take ownership of the strategy, architecture, delivery, and operational performance of its Data & Analytics function. This is a hands-on leadership role overseeing the enterprise data platform, the data products the business relies on, and the team behind them, setting technical direction, delivery standards, and investment priorities while staying personally engaged in key architecture and design decisions. The ideal candidate combines strong technical credibility with senior leadership experience, and will be directly accountable for making the platform AI-ready, ensuring data is governed, traceable, and model-grade to support regulated AI deployment.</p> <h4>Responsibilities:</h4> <p><strong>1. Platform architecture & engineering leadership:</strong><br> Own the enterprise data platform, Azure Data Lake Storage Gen2, Databricks lakehouse (Medallion architecture), Power BI, and Unity Catalog, ensuring it is architecturally sound, standardized, reliable, and engineered to scale.<br> Set and enforce platform engineering standards: ingestion patterns, transformation conventions, Medallion layer contracts, data quality gates, Bronze-to-Gold promotion criteria, and the CI/CD framework that delivers all of it.<br> Own the Unity Catalog governance model, RBAC, lineage, business glossary, and metric definitions, as the platform-enforced foundation for trusted data.<br> Drive the platform roadmap from current state to target, sequencing technical debt remediation, new capability build-out, and platform readiness for downstream AI and analytics demand.<br> Personally lead design reviews and architecture decisions for the platform, engaging directly with the engineering team on complex technical problems where senior technical judgment is required.<br> Own data platform observability and operational excellence, pipeline reliability, SLA adherence, incident response, and data quality monitoring.</p> <p><strong>2. Delivery & data products:</strong><br> Run the D&A delivery programme, from source ingestion and pipeline engineering through to analytics, semantic layer, and data product delivery for business functions.<br> Set and enforce delivery standards: sprint cadence, code review, documentation, testing, validation, and release management practices that govern all team output.<br> Define and own data SLAs to the business, pipeline refresh frequency, availability, and incident response commitments, and ensure delivery is held against them.<br> Own the data integration roadmap, prioritising ingestion of new source systems into the lake in alignment with business demand and platform readiness.<br> Manage complex integration challenges across source systems, engaging directly on technical constraints, working with upstream owners, and designing solutions that align data latency and refresh frequency with business needs.<br> Own the portfolio of data products, datasets, semantic models, and analytics deliverables, ensuring each is documented, well-understood, and fit for the business question it answers.<br> Define and own the standard for what a Gold-layer data product must satisfy to be AI-ready, distinguishing analytics-grade from model-training-grade, with explicit criteria covering completeness, label integrity, statistical consistency, and lineage traceability.</p> <p><strong>3. Data governance & quality:</strong><br> Design and operate the data governance operating model, data ownership, stewardship, quality standards, business glossary, and metric definitions.<br> Embed data quality gates into Medallion layer promotion, making quality a precondition of Bronze-to-Gold progression, with measurable thresholds and clear remediation paths.<br> Own data lineage visibility across the platform so business users can trace the provenance of every number they rely on, end-to-end from source to dashboard.<br> Lead the technical evaluation, selection, and implementation of enterprise data catalogue tooling, including integration with Unity Catalog and the platform metadata layer.<br> Embed appropriate data handling, validation, and audit-trail practices for regulated data domains, partnering with Quality, Regulatory, and Legal to ensure compliance requirements are met by design.<br> Establish data quality measurement as a managed practice, KPIs, dashboards, periodic review, and accountability with data owners.<br> Establish data lineage and provenance practices that satisfy AI explainability and regulatory auditability requirements, including GxP-compliant audit trails for Quality, Manufacturing, and Regulatory AI use cases.</p> <p><strong>4. Team leadership & capability development:</strong><br> Lead, hire, and develop the D&A team, Data Engineers, Analytics Engineers, BI Developers, and Business Analysts, sequencing hires in alignment with the platform roadmap and delivery demand.<br> Define and evolve the role design, skills profile, and career framework for the team within the broader Data & AI CoE structure.<br> Set the team's performance culture: clear ownership, high engineering standards, fast feedback, continuous learning, and documentation as a team discipline.<br> Coach and develop team members directly, identifying high-potential individuals, investing in their technical and leadership growth, and building bench strength across roles.<br> Manage team capacity and allocation across the platform roadmap and business delivery demand, ensuring focus stays on high-value work aligned to strategic priorities.<br> Set the engineering craft culture, code review, design review, pairing, and shared technical standards, that lifts the technical quality of all team output.</p> <p><strong>5. Business partnership & vendor engagement:</strong><br> Serve as the senior D&A point of contact for business function leadership, translating business data needs into platform and delivery priorities.<br> Build credibility with business stakeholders through reliable, consistent delivery, data products that are well-understood, well-documented, and match business expectations.<br> Communicate proactively on platform status, delivery commitments, risks, and trade-offs, ensuring stakeholders have a current view and surfacing issues early.<br> Represent D&A in cross-functional planning forums, ensuring the data foundation perspective is present in enterprise architecture, application, and AI investment decisions.<br> Manage operational vendor and partner relationships for the data platform, Databricks, Microsoft Azure, Power BI, and implementation or augmentation partners.</p> <h4>Requirements</h4> <p>Bachelor's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or related discipline.<br> Master's degree in Data Science, Computer Science, Business Administration, or related field is preferred.<br> Relevant certifications in cloud data platforms (e.g., Azure Data Engineer, Databricks Certified Data Engineer Professional) are preferred.<br> Minimum 10 years of progressive experience in data engineering, data platform, or enterprise analytics roles.<br> Minimum 5 years in a senior leadership role with direct team management accountability, hiring, developing, and performance managing a multi-disciplinary technical team.<br> Proven hands-on production experience with Databricks (Delta Lake, Unity Catalog) and Azure data services at platform-design and engineering-lead level.<br> Proven track record building or substantially remediating a cloud-native data platform in a complex, multi-source enterprise environment.<br> Experience with Medallion/lakehouse architecture patterns in production.<br> Experience designing data products and platform capabilities for AI/ML consumption, including Feature Store design, training dataset engineering, and ML data lineage is preferred.<br> Experience working at the interface of a data platform team and an AI/ML team, translating model requirements into data infrastructure specifications and owning the data readiness handoff is preferred.<br> Experience leading a data governance or catalogue implementation, from design through to business adoption.<br> Life sciences, pharmaceutical, or other regulated industry experience is preferred.</p> <h4>Skills:</h4> <p><strong>Technical competencies:</strong><br> Data platform architecture<br> Data engineering, pipeline design & CDC patterns<br> Data modelling, transformation & engineering standards<br> Data governance, quality & catalogue<br> Analytics & BI delivery<br> MLOps & ML platform foundations<br> Delivery management & agile methods</p> <p><strong>Platform & technical skills:</strong><br> Deep practical expertise in Azure data services: ADLS Gen2, Azure Data Factory, Azure DevOps.<br> Hands-on production experience with Databricks, Delta Lake, notebooks, Unity Catalog, MLflow.<br> Strong understanding of incremental load and CDC patterns across enterprise source systems (SAP, Veeva, SuccessFactors, and similar).<br> Power BI at the semantic layer level, understanding how the semantic layer should be designed for enterprise scale.<br> CI/CD for data pipelines, practical implementation at production scale.<br> Data quality frameworks, profiling, expectation testing, alerting, and remediation workflows.<br> Working knowledge of modern data governance tooling: Unity Catalog, Collibra, Purview, or DataHub.</p> <p><strong>Leadership & business skills:</strong><br> Credible with both technical teams and senior business stakeholders.<br> Strong delivery discipline, owns commitments, communicates risks early, and sizes work realistically.<br> Structured thinker, able to take a complex current state and produce a clear, prioritised, sequenced roadmap.<br> Strong written and verbal communication in English; Arabic proficiency valued.<br> Cultural intelligence for working effectively across MENA, US, and Europe.</p></p><p></p>

<p><h4>Description</h4> <p>A leading global pharmaceutical company is seeking a Director, Data & Analytics to take ownership of the strategy, architecture, delivery, and operational performance of its Data & Analytics function. This is a hands-on leadership role overseeing the enterprise data platform, the data products the business relies on, and the team behind them, setting technical direction, delivery standards, and investment priorities while staying personally engaged in key architecture and design decisions. The ideal candidate combines strong technical credibility with senior leadership experience, and will be directly accountable for making the platform AI-ready, ensuring data is governed, traceable, and model-grade to support regulated AI deployment.</p> <h4>Responsibilities:</h4> <p><strong>1. Platform architecture & engineering leadership:</strong><br> Own the enterprise data platform, Azure Data Lake Storage Gen2, Databricks lakehouse (Medallion architecture), Power BI, and Unity Catalog, ensuring it is architecturally sound, standardized, reliable, and engineered to scale.<br> Set and enforce platform engineering standards: ingestion patterns, transformation conventions, Medallion layer contracts, data quality gates, Bronze-to-Gold promotion criteria, and the CI/CD framework that delivers all of it.<br> Own the Unity Catalog governance model, RBAC, lineage, business glossary, and metric definitions, as the platform-enforced foundation for trusted data.<br> Drive the platform roadmap from current state to target, sequencing technical debt remediation, new capability build-out, and platform readiness for downstream AI and analytics demand.<br> Personally lead design reviews and architecture decisions for the platform, engaging directly with the engineering team on complex technical problems where senior technical judgment is required.<br> Own data platform observability and operational excellence, pipeline reliability, SLA adherence, incident response, and data quality monitoring.</p> <p><strong>2. Delivery & data products:</strong><br> Run the D&A delivery programme, from source ingestion and pipeline engineering through to analytics, semantic layer, and data product delivery for business functions.<br> Set and enforce delivery standards: sprint cadence, code review, documentation, testing, validation, and release management practices that govern all team output.<br> Define and own data SLAs to the business, pipeline refresh frequency, availability, and incident response commitments, and ensure delivery is held against them.<br> Own the data integration roadmap, prioritising ingestion of new source systems into the lake in alignment with business demand and platform readiness.<br> Manage complex integration challenges across source systems, engaging directly on technical constraints, working with upstream owners, and designing solutions that align data latency and refresh frequency with business needs.<br> Own the portfolio of data products, datasets, semantic models, and analytics deliverables, ensuring each is documented, well-understood, and fit for the business question it answers.<br> Define and own the standard for what a Gold-layer data product must satisfy to be AI-ready, distinguishing analytics-grade from model-training-grade, with explicit criteria covering completeness, label integrity, statistical consistency, and lineage traceability.</p> <p><strong>3. Data governance & quality:</strong><br> Design and operate the data governance operating model, data ownership, stewardship, quality standards, business glossary, and metric definitions.<br> Embed data quality gates into Medallion layer promotion, making quality a precondition of Bronze-to-Gold progression, with measurable thresholds and clear remediation paths.<br> Own data lineage visibility across the platform so business users can trace the provenance of every number they rely on, end-to-end from source to dashboard.<br> Lead the technical evaluation, selection, and implementation of enterprise data catalogue tooling, including integration with Unity Catalog and the platform metadata layer.<br> Embed appropriate data handling, validation, and audit-trail practices for regulated data domains, partnering with Quality, Regulatory, and Legal to ensure compliance requirements are met by design.<br> Establish data quality measurement as a managed practice, KPIs, dashboards, periodic review, and accountability with data owners.<br> Establish data lineage and provenance practices that satisfy AI explainability and regulatory auditability requirements, including GxP-compliant audit trails for Quality, Manufacturing, and Regulatory AI use cases.</p> <p><strong>4. Team leadership & capability development:</strong><br> Lead, hire, and develop the D&A team, Data Engineers, Analytics Engineers, BI Developers, and Business Analysts, sequencing hires in alignment with the platform roadmap and delivery demand.<br> Define and evolve the role design, skills profile, and career framework for the team within the broader Data & AI CoE structure.<br> Set the team's performance culture: clear ownership, high engineering standards, fast feedback, continuous learning, and documentation as a team discipline.<br> Coach and develop team members directly, identifying high-potential individuals, investing in their technical and leadership growth, and building bench strength across roles.<br> Manage team capacity and allocation across the platform roadmap and business delivery demand, ensuring focus stays on high-value work aligned to strategic priorities.<br> Set the engineering craft culture, code review, design review, pairing, and shared technical standards, that lifts the technical quality of all team output.</p> <p><strong>5. Business partnership & vendor engagement:</strong><br> Serve as the senior D&A point of contact for business function leadership, translating business data needs into platform and delivery priorities.<br> Build credibility with business stakeholders through reliable, consistent delivery, data products that are well-understood, well-documented, and match business expectations.<br> Communicate proactively on platform status, delivery commitments, risks, and trade-offs, ensuring stakeholders have a current view and surfacing issues early.<br> Represent D&A in cross-functional planning forums, ensuring the data foundation perspective is present in enterprise architecture, application, and AI investment decisions.<br> Manage operational vendor and partner relationships for the data platform, Databricks, Microsoft Azure, Power BI, and implementation or augmentation partners.</p> <h4>Requirements</h4> <p>Bachelor's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or related discipline.<br> Master's degree in Data Science, Computer Science, Business Administration, or related field is preferred.<br> Relevant certifications in cloud data platforms (e.g., Azure Data Engineer, Databricks Certified Data Engineer Professional) are preferred.<br> Minimum 10 years of progressive experience in data engineering, data platform, or enterprise analytics roles.<br> Minimum 5 years in a senior leadership role with direct team management accountability, hiring, developing, and performance managing a multi-disciplinary technical team.<br> Proven hands-on production experience with Databricks (Delta Lake, Unity Catalog) and Azure data services at platform-design and engineering-lead level.<br> Proven track record building or substantially remediating a cloud-native data platform in a complex, multi-source enterprise environment.<br> Experience with Medallion/lakehouse architecture patterns in production.<br> Experience designing data products and platform capabilities for AI/ML consumption, including Feature Store design, training dataset engineering, and ML data lineage is preferred.<br> Experience working at the interface of a data platform team and an AI/ML team, translating model requirements into data infrastructure specifications and owning the data readiness handoff is preferred.<br> Experience leading a data governance or catalogue implementation, from design through to business adoption.<br> Life sciences, pharmaceutical, or other regulated industry experience is preferred.</p> <h4>Skills:</h4> <p><strong>Technical competencies:</strong><br> Data platform architecture<br> Data engineering, pipeline design & CDC patterns<br> Data modelling, transformation & engineering standards<br> Data governance, quality & catalogue<br> Analytics & BI delivery<br> MLOps & ML platform foundations<br> Delivery management & agile methods</p> <p><strong>Platform & technical skills:</strong><br> Deep practical expertise in Azure data services: ADLS Gen2, Azure Data Factory, Azure DevOps.<br> Hands-on production experience with Databricks, Delta Lake, notebooks, Unity Catalog, MLflow.<br> Strong understanding of incremental load and CDC patterns across enterprise source systems (SAP, Veeva, SuccessFactors, and similar).<br> Power BI at the semantic layer level, understanding how the semantic layer should be designed for enterprise scale.<br> CI/CD for data pipelines, practical implementation at production scale.<br> Data quality frameworks, profiling, expectation testing, alerting, and remediation workflows.<br> Working knowledge of modern data governance tooling: Unity Catalog, Collibra, Purview, or DataHub.</p> <p><strong>Leadership & business skills:</strong><br> Credible with both technical teams and senior business stakeholders.<br> Strong delivery discipline, owns commitments, communicates risks early, and sizes work realistically.<br> Structured thinker, able to take a complex current state and produce a clear, prioritised, sequenced roadmap.<br> Strong written and verbal communication in English; Arabic proficiency valued.<br> Cultural intelligence for working effectively across MENA, US, and Europe.</p></p><p></p>

<p>Senior Software Engineer – Flutter and Firebase and Go</p><p>Location: Amman, Jordan – On-site<br>Employment Type: Full-time</p><p>About the Company:</p><p>We are an American company that buys and recycles cars in the Chicago area, with a 10-person operations team in Jordan.</p><p>Our daily operations run on software we develop internally, including dispatch management, tow-truck fleet management, vehicle intake, parts inventory, and payroll and employee management.</p><p>We are seeking a Senior Software Engineer to join our engineering team and help develop, maintain, and operate a live production platform used daily by drivers, dispatchers, and yard staff.</p><p>Responsibilities:</p><ul><li>Develop and maintain Flutter applications for web, Android, and iOS.</li><li>Develop and manage Firebase services, including Firestore, Authentication, Cloud Functions, and FCM.</li><li>Develop and maintain APIs using Go and PostgreSQL.</li><li>Implement features end-to-end from requirements analysis to testing, deployment, and production verification.</li><li>Diagnose operational issues and fix outages affecting users and staff quickly.</li><li>Write clean, maintainable code with clear tests and reviews.</li><li>Prepare small, organized Merge Requests linked to work tickets.</li><li>Manage build and release processes via GitLab CI, Codemagic, TestFlight, and Shorebird.</li><li>Apply best practices for data protection and system security.</li><li>Monitor app and service performance post-release and verify new versions work correctly.</li><li>Collaborate directly with management and operations teams to understand problems and translate them into practical software solutions.</li></ul><p>Basic Requirements:</p><ul><li>Strong, hands-on experience with Flutter and Dart for mobile and web apps.</li><li>Advanced Firebase experience, especially Firestore, Auth, Cloud Functions, and FCM.</li><li>Experience building backend systems with Go, or strong Node.js experience with willingness to work in Go.</li><li>Good PostgreSQL and SQL/database design experience.</li><li>Experience with REST APIs and external system integration.</li><li>Good understanding of Git, GitLab, and CI/CD processes.</li><li>Experience releasing Android and iOS apps and managing versions.</li><li>Ability to handle production systems and data securely and responsibly.</li><li>Ability to work independently and take ownership from start to finish.</li><li>Good written English, especially for tickets and technical documentation.</li><li>Proven hands-on experience shipping real production apps or systems.</li></ul><p>Nice to Have:</p><ul><li>Experience with Shorebird and direct Flutter updates.</li><li>Experience with Codemagic, TestFlight, and Google Play Console.</li><li>Experience with Docker or Google Cloud Platform.</li><li>Experience with maps, notifications, location tracking, or fleet management systems.</li><li>Experience with operational, logistics, or internal enterprise apps.</li></ul><p>Nature of Work:</p><p>This is a hands-on delivery role, not a meetings-focused position. You will be expected to take features from implementation through testing and production verification.</p><p>The position is based at our office in Amman, Jordan. Some overlap with US Central Time will be required. Exact working hours will be discussed during the interview.</p><p>Compensation:</p><ul><li>Competitive salary based on demonstrated experience and technical ability.</li><li>Payment in JOD or USD equivalent.</li><li>Opportunity to work on a real, stable production product used daily.</li><li>Clear ownership, visible output, and direct influence over the product.</li></ul><p>How to Apply:</p><p>Please Send the following</p><ol><li>Your CV.</li><li>Your GitHub or GitLab profile.</li><li>A short note describing the most meaningful production system or application you have shipped.</li><li>A clear explanation of your personal contribution and the technologies you used.</li><li>Your expected salary and availability to start.</li></ol><p>Real production work and repositories matter more to us than certificates.</p><p>&nbsp;</p>