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Mid-level metro role with a generalist title and broad skills increases candidate competition.
Data engineering skills (SQL, pipelines, Databricks) are broadly transferable across industries.
Explicit 3–6 years plus mandatory Databricks, SQL, Python/Java and semantic-layer requirements make screening strict.
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Design, build, and maintain semantic models using Databricks Metric Views to ensure consistent, reusable, and governed metrics.
Develop and optimize data pipelines and transformation logic using SQL, Python, and/or Java for large-scale datasets.
Operate within an AI-driven development lifecycle, leveraging agentic coding tools for coding, testing, refactoring, and documentation, ensuring code correctness and security.
3–6 years of professional software/data engineering experience.
Hands-on experience with Databricks including Metric Views, Unity Catalog, Delta Lake, and Lakehouse architecture.
Strong proficiency in SQL (complex joins, window functions, CTEs, query optimization) and working proficiency in Java and/or Python.
Location requirement: On-site in Bengaluru, Karnataka, India.
Experienced in translating business requirements into technical/data solutions with a strong understanding of semantic/metric layers for consistent reporting.
Comfortable working with large-scale enterprise data platforms, data modeling concepts, and ensuring data quality and performance.
Proficient operating within AI-first development environments using agentic coding tools to enhance engineering velocity and quality.