AWS Lead Data Engineer

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.