Senior Data Engineer

Career Category

Information Systems

Role Description:

Let’s do this. Let’s change the world. We are looking for highly motivated expert Senior Data Engineer who can own the design & development of complex data pipelines, solutions and frameworks with detailed functional knowledge of R&D. The ideal candidate will be responsible to design, develop, and optimize data pipelines, data integration frameworks, and metadata-driven architectures that enable seamless data access and analytics. This role prefers deep expertise in big data processing, distributed computing, data modeling, and governance frameworks to support self-service analytics, AI-driven insights, and enterprise-wide data management.

Roles & Responsibilities:

- Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Engineering for Biotech or Pharma functional knowledge of R&D.

- Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture.

- Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency.

- Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance.

- Ensure data security, compliance, and role-based access control (RBAC) across data environments.

- Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets.

- Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring.

- Implement data virtualization techniques to provide seamless access to data across multiple storage systems.

- Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals.

- Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures.