Forward-Deploy Data Engineer for AI in Regulated Systems

EY

Milwaukee (WI)

Hybrid

USD 107,000 - 177,000

Full time

31 hours ago

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Benefits offered by this job

Medical and dental coverage

Pension and 401(k) plans

Paid time off

Hybrid work model

Job summary

EY's Office of the CTO seeks a Data Engineer within the Advanced Forward Engineering team. You will own data readiness, design ingestion and transformation workflows, and ensure governance across client environments to enable safe AI deployments.

You'll collaborate with Forward Deployed Software Engineers and delivery pods to bridge modern AI needs with legacy data landscapes, balancing speed with compliance while delivering measurable, reliable data services in production.

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 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).