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