Data Engineer

The Data Engineer designs, builds, and operates high‑quality, scalable, and reusable data services that support analytics, AI, and GenAI use cases across the business. Responsibilities include: • Building and maintaining batch, streaming, and event‑driven data pipelines and ingestion frameworks. • Developing modular data models and semantic layers for analytics, BI self‑service, and AI. • Implementing orchestration workflows (e.g., Databricks Workflows) and compute engines such as Spark, SQL, and Python. • Working with storage technologies like Delta Lake, ADLS, feature stores, and vector stores. • Implementing data quality checks, validations, monitoring, and observability using tools such as Great Expectations or Monte Carlo. • Contributing to data lineage, metadata management, and documentation to ensure governance and GDPR compliance. • Delivering curated datasets and reusable assets for analytics, machine learning, and GenAI, including processing of structured, graph, and unstructured data. • Supporting AI Engineering teams with data preparation for embeddings, vector stores, and RAG pipelines. • Contributing to tooling and frameworks (e.g., dbt, Databricks Lakeflow) that enable efficient pipeline development and deployment. • Collaborating with Data Scientists, AI Engineers, Product Owners, Business SMEs, and Platform teams, participating in design discussions, code reviews, and architecture forums, and following best practices for version control, testing, CI/CD, and operational excellence.