Data Engineer

Job Title: Data Engineer
Experience: 9 Years
Work Mode: Hybrid
Location: Hyderabad

Role Summary
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 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.

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.

Required Skills:

Data EngineeringData bricksSQLSparkPythonAIGEN AI