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Tier-1 employer, mid-level data engineer title, and Bangalore metro drive high applicant competition.
Data engineering skills transfer across industries but platform-specific tooling raises moderate specialization.
Multiple mandatory technologies and explicit 5+ years requirement make shortlisting highly strict.
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Designs, builds, and maintains scalable data pipelines and ETL/ELT workflows using Spark, Airflow, Kafka, and Flink on Databricks and AWS.
Develops data platform components including data cataloging, quality frameworks, semantic/metrics layers with governance, lineage, and compliance.
Leads evaluation and architecture sessions across teams and vendors to improve technical solutions and operational stability.
5+ years of professional experience in software engineering or data platform development.
Hands-on experience with Databricks data pipelines, Apache Airflow orchestration, and distributed frameworks like Spark and Flink.
Proficient in Python and SQL programming languages.
Experience with AWS cloud data services (S3, Glue, Redshift, Athena, EMR, Lake Formation) and data modeling (star schema, snowflake).
Experienced in building and operating production-grade data platforms with integrated metadata management and governance using tools like Apache Iceberg, Unity Catalog, or OpenMetadata.
Skilled in agile engineering practices including CI/CD, application resiliency, and security.
Familiarity with advanced data architectures (data mesh, data products), modern observability, semantic layers, and infrastructure-as-code on containerized environments.