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Tier-1 employer, common Data Engineer title, metro location, mid-level experience, and broad skillset.
Core data engineering skills are broadly transferable across industries despite enterprise-function preference.
Explicit 3-5 years plus mandatory AWS/Databricks/Python/Spark skills make filters strict.
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Enhance and deliver high-quality data products and analytics-ready data solutions for Enabling Functions, optimizing data storage, retrieval, and governance.
Define and implement data engineering solutions including data warehouses, lakes, marts, and APIs, collaborating with data architects and enterprise teams.
Design, develop, and integrate RESTful APIs and LLM-powered AI solutions (e.g., OpenAI, AWS Bedrock) into enterprise workflows with vector databases and orchestration frameworks for enhanced analytics.
3-5 years IT experience, including development and production support of AWS cloud-native data lakes and ecosystems.
Strong programming skills in Python, PySpark, SQL; experience with AWS services like Glue Studio, Athena, Redshift, Postgres DB.
Experience with building ETL/ELT pipelines, Databricks, Delta Lake concepts; knowledge of data security, privacy best practices, and API security standards (OAuth 2.0, JWT).
Work Experience Required: 3-5 years as per JD; Location: Hyderabad, India; On-site or hybrid work model requirements as specified (site-essential/site-by-design).
Experienced in enterprise functions data (HR, Finance, Compliance, Procurement) with prior exposure to data governance and analytics product strategy.
Hands-on with advanced AI/ML integrations, including LLM APIs, vector DBs, and orchestration tools, suited for fast-paced, agile/product-based environments.
Comfortable collaborating across global teams, applying up-to-date data engineering trends, and building reusable API frameworks for scalable enterprise solutions.