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Tier-1 brand plus Bengaluru metro increases applicant density despite specialized Spark and lakehouse skill requirements.
Core data engineering skills transfer across industries, though platform and retail-specific context adds moderate domain bias.
Explicit 6+ years plus mandatory Spark, Databricks, Airflow, dbt and data-platform ownership make filtering strict.
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Own design, build, optimization, and operation of large-scale Spark-based batch data processing and ETL pipelines.
Lead architecture, production-quality code delivery, performance tuning, troubleshooting, and operational reliability of data platform components.
Drive AI-native data engineering initiatives including AI-assisted pipeline generation, optimization, and metadata integration while ensuring governance and quality standards.
6+ years of hands-on experience in Data Engineering or Software Engineering building production data systems at scale.
Expertise in Apache Spark / PySpark, Spark SQL, Spark internals, along with strong programming skills in Python, Java, or Scala.
Experience with ETL/ELT pipeline development, orchestration platforms like Airflow or Dagster, Databricks or equivalent Spark-based platforms, and data modeling/lakehouse architectures.
Advanced SQL skills including query optimization and debugging; experience with data quality, metadata, lineage, and governance frameworks.
Senior individual contributor with deep hands-on expertise in Spark, data pipeline engineering, and lakehouse architectures in high-scale production environments.
Comfortable bridging complex technical domains including Spark execution plans, Python, SQL, orchestration DAGs, and metadata/catalog integration while leading engineering best practices and mentoring.
Experienced or interested in integrating AI/LLM-based tools and agentic workflows to automate and improve data engineering processes under strong operational and governance controls.