Data Governance Engineer

Chime Chime · Fintech · San Francisco, CA · Data Engineering

Data Governance Engineer responsible for building and implementing policies, tools, and automation for data quality, trust signals, scorecards, and governance. Focuses on ensuring data integrity, compliance (SOX), and enabling data consumers, with a specific emphasis on leading AI adoption for data governance workflows.

What you'd actually do

  1. Design and implement trust scorecards, trust signals, and quality indicators that give data consumers real-time visibility into data reliability - and partner with producers upstream to embed quality checks at the source before issues propagate downstream.
  2. Own and evolve Chime's data quality strategy end-to-end. Design and partner with engineering teams to implement data quality frameworks and monitoring systems that proactively identify, surface, and resolve data issues at scale.
  3. Create and enforce policies for data classification, quality, and lifecycle management, ensuring data integrity and compliance with SOX and other applicable regulatory standards.
  4. Develop and deploy automation solutions for data governance tasks, such as metadata management, data lineage tracking, access controls, and quality remediation workflows.
  5. Serve as a leader and advocate for AI tooling within the data governance space - identifying opportunities to apply AI to automate governance workflows, improve data context, enhance trust scoring, and accelerate adoption of governance best practices across engineering teams.

Skills

Required

  • 5+ years in data engineering, data governance, or a related field
  • Hands-on experience in data classification, cataloging, quality assurance, and trust frameworks
  • Engineering ability to design and implement data governance and quality processes
  • Demonstrated experience building data trust signals, quality scorecards, or similar frameworks
  • Proficiency in Python or a comparable programming language
  • Experience building scalable data tooling or automation pipelines

Nice to have

  • Terraform
  • AI tooling for data governance

What the JD emphasized

  • data compliance and risk reduction
  • data quality as a product discipline
  • trust signals and scorecards
  • governance workflows upstream
  • data context and metadata are sufficient
  • AI applications
  • production-quality code
  • governance automation
  • data classification
  • cataloging
  • quality assurance
  • trust frameworks
  • Engineering ability to design and implement data governance and quality processes is critical to success
  • SOX compliance
  • regulatory requirements

Other signals

  • data governance
  • data quality
  • trust signals
  • scorecards
  • automation
  • compliance
  • SOX
  • AI adoption for data governance