Senior Data Engineer - Hybrid AI Data Pipelines

EY; Orlando (FL); Hybrid; USD 107,000 - 177,000; Full time

Benefits offered by this job

Hybrid work model

Medical and dental coverage

Pension and 401(k)

Job summary

EY is seeking a Data Engineer to join the Advanced Forward Engineering (AFE) team, embedded with strategic clients in regulated environments. You will own data readiness, design scalable ingestion pipelines, and ensure governance and security for AI deployments.

The role blends data engineering with delivery execution, requiring collaboration with client stakeholders and forward‑deployed engineers to translate complex data landscapes into compliant, production‑ready solutions in a hybrid work

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field.
• 5+ years of data engineering, data integration, or analytics engineering experience.
• Hands-on experience building and operating data pipelines in cloud or hybrid environments.
• Experience with relational databases, data warehouses, data lakes, or enterprise systems.
• Familiarity with data modeling, transformation frameworks, and API-based data access.

Responsibilities

• Serve as the data owner within an AFE delivery pod and ensure data readiness for delivery outcomes.
• Design and implement ingestion, transformation, and access patterns integrating AI systems with client data sources.
• Ensure governance, security, lineage, and access controls in regulated environments.
• Collaborate with Forward Deployed Software Engineers to enable AI workflows in production.
• Diagnose data quality, schema drift, and integration issues in real deployments.
• Contribute reusable data patterns to improve repeatability across pods.
• Support validation, testing, and deployment activities to ensure consistent data flows.

Job description

EY is seeking a Data Engineer to join the Advanced Forward Engineering (AFE) team, embedded with strategic clients in regulated environments. You will own data readiness, design scalable ingestion pipelines, and ensure governance and security for AI deployments.

The role blends data engineering with delivery execution, requiring collaboration with client stakeholders and forward‑deployed engineers to translate complex data landscapes into compliant, production‑ready solutions in a hybrid work