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Tier-1 brand, mid-level generalist data engineer, and metro hiring raise candidate competition.
Platform-specific lakehouse and Glue/Snowflake skills transfer, but insurance/financial domain preference increases specificity.
Explicit 5–8 years plus many mandatory platform and tool requirements make shortlisting highly stringent.
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Design, build, and operate scalable, production-grade data platforms and end-to-end data pipelines on AWS including ingestion, orchestration, transformation, storage, and governance.
Own pipeline orchestration responsibilities such as scheduling, dependency management, retries, SLAs, and monitoring to ensure reliability and cost efficiency.
Partner with data scientists, analysts, and platform teams to deliver well-governed data products that enable analytics and business decisions in Insurance Systems.
5-8 years of hands-on data engineering experience building production data pipelines.
Mandatory experience with data pipeline orchestrators like Apache Airflow, Dagster, or equivalent in production.
Strong proficiency in Python, advanced SQL skills, and experience with relational databases (e.g., PostgreSQL, MySQL).
Strong hands-on experience with AWS services (including Glue, S3, EMR, Lambda, Athena, Kinesis, Redshift, IAM), Apache Spark, Apache Iceberg, and Snowflake.
Experienced in designing and operating large-scale, scalable data solutions and lakehouse architectures specifically on AWS.
Familiar with data governance, cataloging, and cost optimization in multi-service data ecosystems.
Comfortable collaborating cross-functionally with both technical teams (data scientists, analysts) and business stakeholders within financial or insurance domains.