Job Title

1. Position SummaryThe Data Engineering Lead is responsible for designing, building and operating data platforms and pipelines required for AI, analytics and automation use cases. The role leads a team of Data Engineers to implement the data architecture defined by the Architecture Lead and Solution Architect, ensuring reliable, high-quality and well-governed data for use cases such as Manufacturing Control Tower, Golden Batch, Yield Improvement, OOS prediction and pricing models.2. Roles Played- Data Engineering team lead for AI and analytics initiatives. - Owner of data pipelines, transformations and data availability SLAs. - Key contributor to data modelling, data quality and governance practices.3. Key Platforms / Technologies- Azure Data Lake, Data Warehouse, Synapse, Databricks (or equivalent). - ETL/ELT tools and orchestration frameworks. - Streaming and batch data ingestion frameworks. - BI environments (Power BI, Tableau) as data consumers. - Integration with SAP ECC or S/4 HANA , MES, LIMS, Empower, CTMS and other enterprise sources.4. Overall Job ResponsibilitiesA. Data Platform & Pipeline Design (≈ 25%)- Design data ingestion, transformation and storage solutions as per architecture guidelines. - Build data models and structures to support AI, analytics and reporting use cases. - Ensure solutions are modular, reusable and performance optimized.B. Data Engineering Delivery & Operations (≈ 25%)- Lead the implementation of data pipelines (batch and streaming) into the data lake/warehouse. - Set up and maintain orchestration, monitoring, alerting and logging for data flows. - Own day-to-day data operations and incident resolution related to pipelines.C. Data Quality, Governance & Security (≈ 20%)- Implement data quality rules, validations and reconciliations. - Ensure data lineage and metadata capture for key datasets. - Work with Security and Governance teams to enforce access control, privacy and data integrity requirements.D. Performance, Optimization & Cost Management (≈ 10%)- Optimize pipelines and queries for performance and cost (compute, storage). - Periodically review resource utilization and recommend improvements.E. Collaboration & Stakeholder Engagement (≈ 10%)- Collaborate with Solution Architect, Data Scientists, Full Stack Developers and Business Analysts. - Understand data needs of each use case and translate them into engineering tasks.F. Team Leadership & Capability Building (≈ 10%)- Lead and mentor Data Engineers and ETL developers. - Promote best practices in coding, testing, documentation and DevOps for data.5. External Interfaces- Data platform vendors and implementation partners.6. Internal Interfaces- Solution Architect and Architecture Lead. - BI Developers, Data Scientists and Modelers. - Application team (SAP, MES, LIMS etc.), Infrastructure and Security.8. Education- Bachelor’s degree in engineering / computer science / IT – mandatory. - Postgraduate qualification in Data Engineering / Data Science – preferred.9. Experience- 8–12 years of experience in data engineering, ETL or data warehouse roles. - Minimum 3–5 years leading data engineering teams or complex data projects. - Hands-on experience with cloud data platforms (preferably Azure). - Pharma or manufacturing data experience is an advantage.10. Knowledge & Skills (Functional / Technical)- Strong skills in SQL/NoSQL, Spark/Databricks and ETL tools (ADF, Data Build Tool, SSIS – SQL Server Integration Services); streaming (Kafka/Event Hubs) - Experience with Azure data lake / Storage and Microsoft Fabric Delta Lake, data warehouse architecture - Familiarity with Python/Scala or other scripting for data pipelines. - Understanding data quality, governance and metadata concepts. - Exposure to integration with SAP, MES, LIMS or similar systems.11. Leadership / Managerial Attributes- Hands-on technical leader with a delivery focus. - Good communication and coordination with multiple teams. - Ability to mentor junior engineers and enforce engineering discipline.12. Other Requirements- Relevant cloud/data certifications (e.g., Azure Data Engineer) preferred. - Willingness to support critical data operations outside normal hours when required.