Description

• 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 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.
• Exp :- 9-12 years

Essential Job Functions:
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 using Spark, SQL, and Python.
• Work with storage technologies such as Delta Lake, ADLS, feature stores, 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 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.
• Promote 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.

Qualifications:
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

Key Skills

Education

Bachelor's degree