Data Engineer (Rif.1156)
Core
Design, develop, and maintain scalable data pipelines for ingestion, transformation, and distribution to support reporting, analytics, and future AI/ML initiatives.
Role type
Data Engineer
Builds
Scalable ETL/ELT pipelines, data warehouses, data lakehouses, and data models for decision-making and analytics.
Domain
Pharmaceutical industry / Data Engineering
Deliverable
production ML models | product features | dashboards & analysis
Required skills
Python, SQL, ETL/ELT pipeline design, data warehouse architecture, Apache Spark, PySpark, cloud data platforms (Azure/AWS/GCP), data modeling, data quality controls, data lineage
Preferred skills
Data governance, GDPR compliance, workflow orchestration, DevOps, Git, CI/CD, MLOps, containerization (Docker/Kubernetes), generative AI data patterns (LLM/RAG/embeddings)
Technologies
Apache Spark, PySpark, Azure, AWS, Google Cloud Platform, Docker, Kubernetes, Git
Responsibilities
Design and maintain scalable data pipelines for ingestion, transformation, and distribution; Integrate heterogeneous data sources from enterprise systems (ERP, MES, APIs); Define and maintain data models, schemas, and metadata; Monitor production pipelines for performance and reliability; Implement automatic data quality controls and lineage tracking; Collaborate with Data Architects and Data Scientists on data solutions.
Seniority
Mid-level to Senior, hands-on IC
