AWS Lead Data Engineer
exavaluNewark (NJ)
About the role
Summary:
Lead the design and delivery of scalable data engineering solutions on AWS, supporting enterprise data onboarding and domain-oriented data products. The role will provide hands-on technical leadership across AWS-native data services and work with Prudential teams to build trusted, reusable and analytics/AI-ready data assets.
Key Responsibilities
Technical Leadership & Architecture:
Lead the design and implementation of data ingestion, transformation and processing solutions using AWS native services including S3, Glue, EMR, Redshift and Athena.
Define appropriate engineering patterns for batch and large-scale data processing using Python and PySpark.
Guide implementation of modern lake/lakehouse patterns, including S3 and Apache Iceberg, where applicable.
Provide technical direction across design, development, testing, deployment and production support.
Data Engineering & Data Products:
Lead onboarding of data from legacy and enterprise source systems into AWS. Drive development of reusable pipelines and engineering components for specific enterprise data domains.
Support the
ADS / Authoritative Data Source:
approach, where domain data is packaged as trusted data products for enterprise consumption. Ensure datasets produced by the engineering team are consumable for downstream analytics and AI use cases.
Engineering Quality:
Conduct design and code reviews for Python/PySpark and AWS data-engineering components.
Establish development standards, reusable patterns and engineering best practices.
Ensure appropriate data validation and reconciliation are incorporated into pipelines.
Troubleshoot performance and scalability issues across data pipelines and processing workloads.
Team Leadership & Collaboration:
Provide day-to-day technical guidance to Data Engineers.
Break functional/data requirements into engineering tasks and coordinate delivery.
Work closely with Prudential's architecture, data, technology and business-aligned teams.
Communicate technical risks, dependencies and design decisions to stakeholders.
Mentor engineers and build the team's AWS/Python/PySpark engineering capability.
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