Senior Software Engineer, Data Governance

United States Digital Space LLC; San Francisco (CA); On-site; USD 160,000 - 221,000; Full time

Benefits offered by this job

Bonus

Equity

Benefits

Job summary

United States Digital Space LLC is seeking a Data Governance Engineer in San Francisco to build and operationalize data governance frameworks. The role focuses on trust signals, quality metrics, and scalable governance automation using Terraform and Python.

You will collaborate with data engineers, analytics, product engineering, and compliance teams to enable secure, compliant data usage across the company.

Qualifications

• 5+ years in data engineering, data governance, or related field with hands-on experience in data classification, cataloging, quality assurance, and trust frameworks.

Responsibilities

• Build trust signals, scorecards, and quality indicators to give data consumers visibility into data reliability.
• Design and implement data governance policies for classification, quality, and lifecycle management with regulatory alignment (SOX).
• Automate governance processes including metadata management, data lineage, access controls, and quality remediation workflows.

Skills

Data governance

Python

Data quality

Data catalog

Cloud data warehousing

Tools

Terraform

Snowflake

Automation tooling

Job description

About the Role

The Data Governance function is pivotal in ensuring the integrity, trustworthiness, and effective management of the company’s data assets. Our mission is to establish and operationalize data governance frameworks that not only meet compliance requirements, but actively enable high-confidence decision making across the company. As a Data Governance Engineer, you will develop and implement policies and tools for data quality, developer enablement, certified datasets, and governance automation - with a sharp focus on building trust signals and scorecards that help data consumers quickly understand and act on the reliability of the company's data.

This is a hands-on engineering role. You will write production-quality code to build automation for governance and data quality processes using Terraform, Python (or similar languages), and contribute to internal libraries, frameworks, and orchestration workflows that enable scalable governance.

You will partner closely with data engineering, analytics, product engineering, and compliance teams to:

• Support the company in data compliance and risk reduction initiatives

• Establish data quality as a product discipline

• Build trust signals and scorecards that make data reliability transparent and data context valuable for a variety of use cases

• Implement governance workflows upstream, where data are created

• Ensure data context and metadata are sufficient for downstream use cases, including AI applications

You will be involved in reviews of technical designs and implementation plans to provide guidance on appropriate development from a data governance and compliance perspective, acting as a trusted advisor to teams innovating at the company. You will also be a strong advocate and leader for Artificial Intelligence tooling and adoption at the company, bringing curiosity and a practical eye for where AI creates leverage in data governance workflows. This role is 40% strategic influence, 40% development and building, and 20% stakeholder management and support.

The base salary for this role and level of experience will begin at $160,000.00 and go up to $221,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to

• 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.

• Drive Data Quality Strategy: Own and evolve the company'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.

• 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.

• Enable the Data Catalog: Collaborate on the development, adoption, and enrichment of the company's data catalog - improving discoverability, context richness (lineage, ownership, definitions, usage guidance), and the overall experience of working with the company's data.

• 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.

• Champion SOX Compliance: Play an active role in the company's SOX compliance efforts, ensuring that data governance practices, controls, and audit trails meet regulatory requirements and are well-documented.

• 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.

• Collaborate Across Teams: Work closely with data engineers, analysts, and compliance officers to align data governance initiatives with business needs and regulatory requirements.

To thrive in this role, you have

• 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 the company'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 t