Data Governance Engineer

About This RoleAI processing…

Key Responsibilities

• 1
  Support Chime in data compliance and risk reduction initiatives
• 2
  Establish data quality as a product discipline
• 3
  Build trust signals and scorecards that make data reliability transparent and data context valuable for a variety of use cases
• 4
  Implement governance workflows upstream , where data are created
• 5
  Ensure data context and metadata are sufficient for downstream use cases, including AI applications
• 6
  Build Data Trust Infrastructure: 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.
• 7
  Drive Data Quality Strategy: 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.
• 8
  Develop and Implement Data Governance Policies: Create and enforce policies for data classification, quality, and lifecycle management, ensuring data integrity and compliance with SOX and other applicable regulatory standards.
• 9
  Enable the Data Catalog: Collaborate on the development, adoption, and enrichment of Chime's data catalog - improving discoverability, context richness (lineage, ownership, definitions, usage guidance), and the overall experience of working with Chime's data.
• 10
  Automate Governance Processes: Develop and deploy automation solutions for data governance tasks, such as metadata management, data lineage tracking, access controls, and quality remediation workflows.
• 11
  Champion SOX Compliance: Play an active role in Chime's SOX compliance efforts, ensuring that data governance practices, controls, and audit trails meet regulatory requirements and are well-documented.
• 12
  Lead AI Adoption for Data Governance: 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.
• 13
  Collaborate Across Teams: Work closely with data engineers, analysts, and compliance officers to align data governance initiatives with business needs and regulatory requirements.

Requirements

• Experience: 5+ years in data engineering, data governance, or a related field, with hands-on experience in data classification, cataloging, quality assurance, and trust frameworks. Engineering ability to design and implement data governance and quality processes is critical to success.
• Trust & Quality Expertise: Demonstrated experience building data trust signals, quality scorecards, or similar frameworks that make data reliability visible and actionable for both engineers and non-technical stakeholders.
• Programming Experience: Proficiency in Python or a comparable programming language, and experience building scalable data tooling or automation pipelines.
• Curiosity and Relentlessness: You are genuinely curious about data, technology, and the problems that sit at the intersection of the two. You don't let ambiguity stop you - you dig in, ask the right questions, and keep pushing until you've found the right answer or built the right solution. You bring energy and persistence to hard problems and don't need external pressure to drive yourself forward.
• Composure in Complexity: Data governance at a fintech means navigating competing priorities, regulatory constraints, sprawling data ecosystems, and stakeholders with different mental models of "good." You thrive in this environment rather than shrinking from it - you can hold complexity without getting overwhelmed, break it into manageable pieces, and make steady progress without waiting for perfect conditions.
• Exceptional Communication: You are a terrific communicator in both verbal and written form. You can write a crisp one-pager that earns buy-in from a skeptical audience, run a meeting that ends with clarity instead of confusion, and translate deeply technical concepts into plain language that moves people to action. You treat communication as a core part of the job, not an afterthought.
• Product Management Mindset: You think like a product manager as much as an engineer. You ask "who is this for and what problem does it solve?" before writing a line of code. You prioritize ruthlessly, define success before you start building, and care about adoption and outcomes - not just delivery. You treat your internal stakeholders as customers and design solutions with their experience in mind.
• AI Curiosity and Advocacy: Genuine curiosity about AI and its applications in the data governance space, with an appetite for identifying and piloting new tools and workflows that improve the experience of working with Chime's data.
• Technical Proficiency: Strong knowledge of data governance tools and platforms, and experience with cloud data warehouses like Snowflake. Experience with data catalog platforms and driving adoption across engineering organizations is a strong plus.
• Bonus: Compliance Knowledge: Familiarity with SOX, CCPA, and other data privacy and financial regulations, and ability to apply technical skills to governance practices that meet audit and compliance requirements.

Perks & Benefits