Back
Tier-1 brand, metro location, generalist data title, and broad tech requirements increase candidate competition.
Core data engineering skills transfer across industries, though financial/insurance domain preference adds some bias.
Explicit 8+ years requirement and many mandatory technologies and orchestration experience make shortlisting highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and operate scalable production data pipelines and lakehouse platforms on AWS using Apache Iceberg, Spark, Glue, and Snowflake.
Own end-to-end pipeline orchestration including scheduling, dependency management, retries, SLAs, and observability using a data pipeline orchestrator.
Partner with data scientists, analysts, and platform teams to deliver reliable, governed, and cost-efficient data products impacting insurance systems analytics and business decisions.
Minimum 8 years of hands-on data engineering experience building production-grade data pipelines.
Proficiency in Python, advanced SQL, and experience with relational databases.
Mandatory experience operating a data pipeline orchestrator in production (e.g., Apache Airflow, Dagster).
Strong expertise with Apache Spark, Apache Iceberg, AWS Glue (including Data Catalog), Snowflake, and AWS cloud services including S3, EMR, Lambda, Athena, Kinesis, Redshift, and IAM.
Deep experience architecting and managing large-scale data lakehouse platforms on AWS in financial services or insurance domain preferred though not mandatory.
Proven ability to manage complex pipeline orchestration with focus on performance, reliability, and data governance.
Experience mentoring engineers and influencing platform architecture and engineering best practices within a cross-functional, agile environment.