Advanced Forward Engineering - Data Engineer - Senior

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The opportunity In EY’s Office of the CTO, the Advanced Forward Engineering (AFE) team is comprised of cells of Forward Deployed Engineers (FDEs) that are embedded with our strategic customers to ensure the best possible customer experience in using EY’s products. We are seeking a Data Engineer to join AFE delivery pods, where AI‑native systems are deployed directly into highly regulated client environments such as tax, finance, and risk. This role is responsible for ensuring that data is accessible, governed, and production‑ready so AI solutions can be safely and reliably delivered in real‑world conditions. Unlike traditional data engineering roles that focus on centralized platforms, the AFE Data Engineer operates within forward‑deployed pods, working directly with delivery engineers and client stakeholders to integrate enterprise AI systems with legacy data environments. This role is ideal for someone who thrives at the intersection of data engineering, delivery execution, and regulatory constraints, and who can translate ambiguous data landscapes into dependable, compliant solutions.   Your key 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).

Skills and attributes for success

Deep expertise with industry standard database and data management systems (Postgres, Oracle, Databricks, SnowFlake, BigQuery, RedShift, Azure Data Factory, Kafka, Flink, Spark). Strong foundation in data engineering concepts applied to real production systems. Capable of working effectively with imperfect, heterogeneous, and legacy data environments. Experience in balancing delivery speed with data governance and compliance requirements. Pragmatic problem‑solver who can operate with ambiguity and incomplete information. Clear communicator able to explain data constraints and tradeoffs to engineers and stakeholders. Bias toward building durable, well‑understood data interfaces rather than one‑off solutions. Curiosity and flexibility when working with evolving AI and data use cases.

To qualify you must have

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