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Metro location and common Data Engineer role increase competition; seniority and niche AWS Glue skills moderate applicant density.
Core data engineering and AWS skills are transferable, but compensation/finance domain knowledge increases specificity.
Mandatory 8+ years plus specific AWS Glue, Python, and data engineering requirements make shortlisting highly selective.
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Design and implement end-to-end data pipelines and ETL/ELT workflows using Python and AWS cloud services to support compensation calculations and data processing.
Develop event-driven workflows with AWS EventBridge, SQS, SNS, and maintain data quality, auditing, and monitoring layers for high data integrity.
Collaborate with architects, product teams, and engineering partners under Agile practices to build scalable, resilient, cloud-native systems and support compensation platform modernization.
8+ years of experience designing and implementing complex systems, with 5+ years in data engineering in transformation-heavy or rules-driven domains.
Strong Python development skills specifically for ETL/ELT and pipeline orchestration.
Hands-on experience with AWS services including AWS Glue, Lambda, EventBridge, Amazon RDS, S3, and related data lake patterns.
Strong SQL expertise in stored procedures, indexing, and optimization; Bachelor’s degree in Computer Science or related field.
Experienced in building scalable, automated cloud-native data pipelines primarily on AWS, focusing on compensation or rules-driven data processing.
Proficient in SQL and Python for complex data transformations and workflow orchestration within financial or similarly regulated domains.
Comfortable working in Agile teams collaborating closely with business stakeholders, architects, and engineering partners to enhance enterprise compensation platforms.