Data Engineers

FIS•Jacksonville, FL

1d•Hybrid

About The Position

The Data Engineer, AI Governance will design, build, and maintain data foundations that support responsible, trusted, and scalable AI across FIS. The role combines backend engineering with a focus on data quality, governance, and transparency. It ensures data is discoverable, traceable, reliable, and fit for purpose, with visibility into enterprise data lineage, ownership, and usage. The position is based in Jacksonville, Florida, with a hybrid schedule requiring onsite work Tuesday through Thursday. Current and future sponsorship are not available.

Requirements

Experience as a Data Engineer, Data Platform Engineer, Data Integration Engineer, or in a similar role supporting large-scale data environments.
Strong experience building and supporting backend data pipelines using modern data engineering practices.
Experience integrating, transforming, cleansing, and consolidating data across multiple sources.
Strong understanding of data governance, data quality, metadata management, and data lifecycle management.
Experience establishing and documenting data lineage, data flows, and data dependencies.
Proficiency with SQL and data modeling concepts in enterprise data environments.
Experience with cloud-based data platforms, data warehouses, and data lake architectures.
Knowledge of data security, access management, and governance principles.
Strong analytical, troubleshooting, and problem-solving skills, with exceptional attention to detail.
Passion for creating high-quality, trusted, and well-governed data products.

Nice To Haves

Experience supporting AI, machine learning, or responsible AI governance programs.
Familiarity with data catalog, metadata management, data lineage, or data observability platforms.
Experience implementing enterprise data quality and monitoring frameworks.
Experience in financial services, fintech, payments, banking, or highly regulated industries.

Responsibilities

Design, develop, and maintain scalable backend data pipelines, integrations, and data platforms for AI governance and analytics initiatives.
Consolidate and transform data from multiple enterprise sources into trusted, governed datasets for AI and business use.
Establish and maintain end-to-end data lineage, showing data origins, transformations, and consumption.
Partner with business, technology, and governance teams to define data ownership, stewardship, and accountability models.
Implement data quality controls, monitoring frameworks, and observability practices to identify and resolve data issues proactively.
Develop metadata management processes that improve data discoverability, traceability, and governance compliance.
Support data access governance by helping define, automate, and monitor access controls and permissions.
Build and maintain data validation, reconciliation, and audit capabilities to improve trust in enterprise data assets.
Collaborate with AI, machine learning, and analytics teams to make governed data available for model development and operational use cases.
Recommend and implement automation opportunities to improve data reliability, efficiency, and operational excellence.