Data Engineer

Build and maintain scalable data pipelines and ingestion frameworks for batch,
streaming, and event-driven data.
• Develop and maintain modular data models and semantic layers optimized for
analytics, BI self-service and AI use cases.
• Implement and operate orchestration workflows (e.g., Databricks Workflows)
and compute engines (Spark, SQL, Python).
• Work with storage technologies such as Delta Lake, ADLS, feature and vector
stores.
Data Quality, Governance & Observability
• Implement data quality checks, validations, and monitoring to ensure reliability
and trust in data products.
• Contribute to data lineage, metadata management, and documentation.
• Apply observability practices using tools such as Great Expectations or Monte
Carlo.
• Ensure compliance with data governance standards and regulations (e.g., GDPR)
in collaboration with data governance teams.
Enablement for AI & Analytics Use Cases
• Deliver curated datasets and reusable data assets for analytics, machine
learning, and GenAI applications.
• Build pipelines that process structured, graph, and unstructured data (e.g., text,
documents, images).

• Support AI Engineering teams with data preparation for embeddings, vector
stores, and retrieval-augmented generation (RAG) pipelines.
Tooling & Self-Service
• Contribute to data engineering tooling and frameworks that enable eSicient
development and deployment of pipelines.
• Develop data pipelines using tools such as dbt and Databricks Lakeflow.
• Support reuse of data services through clear documentation, data contracts,
templates, and examples.
Collaboration & Ways of Working
• Collaborate with Data Scientists, AI Engineers, Product Owners, Business SMEs,
and Platform teams.
• Participate in technical design discussions, code reviews, and architecture
forums.
• Follow engineering best practices for version control, testing, CI/CD, and
operational excellence.