The Data Engineer is responsible for designing, building, and operating high-quality, scalable, and reusable data services that support analytics, AI, and Gen AI 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 Gen AI 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/Gen AI 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-productive, 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 Gen AI innovation.

📌 Data Engineer (Hyderabad)
🏢 The HEINEKEN
📍 Hyderabad