Forward-Deployed Data Engineer for AI Pipelines

EY

Hoboken (NJ)

Hybrid

USD 120,000 - 163,000

Full time

2 days ago

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Job summary

EY’s Office of the CTO is seeking a Data Engineer to join the Advanced Forward Engineering team. You will embed with client delivery pods to ensure data readiness, design robust ingestion and transformation pipelines, and enforce governance and security in AI deployments.

Work within forward-deployed pods, collaborating with engineers and client stakeholders to integrate enterprise data with legacy systems, while navigating regulatory constraints and delivering reliable data solutions.

Qualifications

Bachelor’s or Master’s degree in Computer Science or related technical field.
5+ years of experience in data engineering, data integration, or analytics engineering roles.
Hands-on experience building and operating data pipelines in cloud or hybrid environments.
Experience integrating with relational databases, data warehouses, data lakes, or enterprise systems.
Familiarity with data modeling, transformation frameworks, and API-based data access.
Experience working under data governance, security, or compliance constraints.
Ability to collaborate closely with application engineers and operate in delivery-focused teams.

Responsibilities

Serve as the data owner within an AFE delivery pod, ensuring data readiness does not block or delay delivery outcomes.
Design and implement data ingestion, transformation, and access patterns that integrate AI systems with client and legacy data sources.
Ensure data pipelines comply with governance, security, lineage, and access control requirements mandated by regulated environments.
Implement data interfaces and contracts that satisfy DevOps Specification (DS) data requirements for supported deployment templates.
Partner closely with Forward Deployed Software Engineer roles to enable AI workflows, retrieval, and analytics that are reliable in production.
Diagnose and resolve data quality, schema drift, and integration issues encountered in real deployment scenarios.
Contribute reusable data patterns and implementation learnings back to the AFE integration core to improve repeatability across pods.
Support validation, testing, and deployment activities to ensure data flows behave correctly and consistently across environments (dev, test, pre-prod, prod).