Data Engineer

The Data Engineer is responsible for designing, building, and operating high-quality, scalable, and reusable data services that support analytics, AI, and GenAI use cases across business domains. In this role, you will design and work hands-on with data pipelines, data models, orchestration frameworks, storage layers, and observability tooling. You will collaborate closely with AI Engineers, Data Scientists, Product Owners, and Platform teams to deliver reliable, well-governed, and self-service data products. Key Responsibilities Data Platform & Services Engineering • 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 efficient 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. Preferred Qualifications • 5+ years of experience in data engineering and building production-grade data pipelines. • Strong hands-on experience with data platforms such as Databricks. • Solid knowledge of data modeling, SQL, Spark, and Python. • Experience with orchestration frameworks, data quality tooling, and observability practices. • Exposure to unstructured data processing and AI/GenAI data pipelines is a strong plus. • Experience working in a global, multi-team environment is beneficial.

Success in This Role Means • Reliable, well-documented data products are available for analytics and AI use cases. • Data pipelines are scalable, cost-efficient, observable, and easy to operate. • Data engineers and AI teams can move faster using reusable patterns and self-service data services. • Structured and unstructured data are effectively integrated to support advanced analytics and GenAI innovation.