AWS Data Engineer

Exavalu; Newark (NJ); Presencial; USD 140.000 - 190.000; Jornada completa

Descripción de la vacante

Exavalu in Newark, NJ is seeking a Senior Data Engineer to lead the design and delivery of scalable data engineering solutions on AWS, focusing on enterprise data onboarding and domain-oriented data products for analytics and AI readiness.

You will provide hands-on technical leadership across AWS-native services (S3, Glue, EMR, Redshift, Athena), drive lakehouse patterns, and mentor engineers while collaborating with architecture, data, technology and business teams to ensure robust, validated

Formación

• Hands‑on experience building data pipelines on AWS.
• Experience leading data engineering teams or initiatives.
• Deep knowledge of batch and streaming data processing patterns.

Responsabilidades

• Lead the design and delivery of AWS-based data ingestion, transformation and processing solutions.
• Define engineering patterns for large-scale data processing using Python and PySpark.
• Guide lakehouse patterns with S3 and Apache Iceberg where applicable.
• Provide technical direction across design, development, testing, deployment and production support.
• Mentor Data Engineers and drive AWS/Python/PySpark excellence.

Conocimientos

Python

PySpark

AWS

Data Pipelines

Data Lakes

Herramientas

S3

Glue

EMR

Redshift

Athena

Descripción del empleo

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.
• 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.
• 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.
• 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.