Description

A leading global pharmaceutical company is seeking an experienced data engineer / BI engineer to design, build, and maintain scalable data platforms that enable analytics, reporting, and data-driven decision making. The role focuses on data ingestion, transformation, modeling, and analytics enablement, with strong emphasis on Python-based data engineering and modern data architectures.

Responsibilities:

1. Data engineering & data architecture:
Design and maintain scalable data pipelines for ingesting, transforming, and serving structured and semi-structured data.
Develop robust data transformation and processing logic using Python and distributed data processing frameworks.
Design and maintain analytical data models (dimensional models, fact/dimension tables) optimized for reporting and analytics.
Ensure data quality, reliability, performance, and consistency across the data platform.
Translate business and analytical requirements into efficient and maintainable data solutions.

2. Analytics & BI enablement:
Build and maintain semantic / analytical models that support enterprise reporting and self-service analytics.
Collaborate with analysts and business users to deliver trusted datasets and metrics.
Support reporting and dashboarding solutions by providing optimized data structures and calculations.

3. Delivery & collaboration:
Produce clear technical documentation including data models, transformation logic, and pipeline designs.
Collaborate with cross-functional teams including data analysts, data engineers, and business stakeholders.

Requirements

Bachelor’s degree in data science, computer science, data engineering, or a relevant field.
1-3 years of BI development experience.
3+ years of cloud-based BI development experience.
Excellent English language verbal and written communication.
Able to analyse and understand complex data.
Able to implement modules that have security and authorization frameworks.
Strong problem-solving skills.

Technical skills:

Programming & data processing:
Strong proficiency in Python for data engineering and analytics use cases.
Experience building reusable, testable, and maintainable data transformation code.

Data warehousing & modeling:
Strong knowledge of SQL and analytical query optimization.
Solid understanding of data warehousing concepts (ETL/ELT, fact and dimension modeling).
Experience designing star and snowflake schemas for analytics and reporting.
Understanding of semantic layers and metrics definitions for BI consumption.

Cloud & modern data platforms:
Experience working with cloud-based data platforms and data lakes (preferred Azure).
Understanding of scalable data architectures (Lakehouse, warehouse, streaming vs batch).

BI & analytics:
Experience supporting BI and reporting tools through well-designed datasets and models (preferred Power BI).
Knowledge of analytical calculations, KPIs, and performance optimization for reporting workloads.

CI/CD & DevOps (supporting skills):
Working knowledge of CI/CD concepts applied to data and analytics projects.
Experience using version control systems (e.g., Git, Perforce or Apache Subversion) for managing data, code, and artifacts.
Familiarity with deployment pipelines and environment promotion for data solutions.