We are looking for an experienced Data Engineering Lead to lead our Data Engineering team and define the data foundations powering enterprise analytics, AI, and Generative AI solutions.
The role combines team leadership, data architecture, enterprise data integration, governance, and AI-ready data platforms.
Key Responsibilities
• Lead and mentor the Data Engineering team and establish engineering standards and architecture guidelines.
• Define enterprise data architecture across Data Lakes, Data Warehouses, Data Marts, and Lakehouse platforms.
• Design and oversee ETL/ELT, batch and real-time pipelines, CDC, data integration, and streaming architectures.
• Establish Data Governance, covering data quality, lineage, metadata, cataloguing, ownership, classification, access control, security, and lifecycle management.
• Build the data foundation required for GenAI applications, including RAG, vector databases, embeddings, document ingestion, enterprise knowledge bases, semantic search, knowledge graphs, and AI context/memory layers.
• Define data modeling and semantic standards supporting BI, analytics, AI, and operational applications.
• Evaluate technologies and architectures while balancing scalability, performance, security, governance, cost, and maintainability.
Required Experience
• Strong experience in Data Engineering and Data Architecture, with proven team leadership experience.
• Deep knowledge of Data Warehouses, Data Lakes, Data Marts, and Lakehouse architectures.
• Strong experience with enterprise ETL/data integration platforms such as Informatica PowerCenter / Informatica IDMC, IBM DataStage, Talend, SSIS, Oracle Data Integrator (ODI) or equivalent.
• Experience with technologies such as Spark, Kafka, Databricks, Snowflake, Microsoft Fabric/Synapse, BigQuery, Redshift, Airflow, and dbt.
• Strong understanding of SQL, data modeling, ETL/ELT, CDC, APIs, batch processing, streaming, and data quality.
• Experience with data governance and cataloguing platforms such as Informatica Data Governance & Catalog, Collibra, Microsoft Purview, Alation, or similar.
• Experience with cloud data platforms across Azure, AWS, and/or GCP
• Good understanding of GenAI data architectures including RAG, vector search, embeddings, knowledge graphs, and enterprise AI data pipelines.
