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Manager, Data Governance

Job in Calgary, Alberta, D3J, Canada
Listing for: Benevity
Full Time position
Listed on 2026-08-17
Job specializations:
  • IT/Tech
    Information & Knowledge Management, Data Engineering
Salary/Wage Range or Industry Benchmark: 110000 - 170000 CAD Yearly CAD 110000.00 170000.00 YEAR
Job Description & How to Apply Below

Meet Benevity

Benevity is the way the world does good, providing companies (and their employees) with technology to take social action on the issues they care about. Through giving, volunteering, grantmaking, employee resource groups and micro-actions, we help most of the Fortune 100 brands build better cultures and use their power for good. We’re also one of the first B Corporations in Canada, meaning we’re as committed to purpose as we are to profits.

We have people working all over the world, including Canada, Spain, Switzerland, the United Kingdom, the United States and more!

Benevity's software architecture has evolved to include a diverse technology stack. The front end application, using mainly VueJS, is designed for both desktop and mobile web rendering. Our back end systems (some Java Spring Boot, some PHP) manage data processing, interfaces with external providers, and ensures robust security. We run and operate our systems in AWS and GCP cloud, leveraging where possible cloud native technology.

We emphasize clean, maintainable code and use GIT for version control and collaboration. Additionally, our platform integrates with various external services for functionalities like email communication, content storage, and server to server interactions.

Our culture is driven by our core value of  we are we  and as a Manager of Data Governance you will work in an outcome driven environment where collaboration with your product, design and engineering counterparts is paramount.

If you are eager to make a difference and thrive in a collaborative setting, we invite you to join our team!

Position Overview

Data Governance exists so that the rest of the organization can trust what they are working with. The Manager of Data Governance sets the rules, owns the standards, and holds everyone accountable. This role is the organizational authority on what data exists, who owns it, where it came from, and who can access it. On quality specifically, this team defines what good looks like.

Engineering and Data Ops make it happen. Governance checks whether it actually did. The ideal candidate is a confident cross functional leader who can build programs, influence without authority, and translate policy into practice across a complex data organization.

What You'll Do
  • Lead and grow the Data Governance function, setting the strategic direction for data ownership, stewardship policy, access governance, and data classification standards across the organization.
  • Own and operate Data Hub as the living catalog of the organization's data assets, covering metadata management, catalog onboarding, tagging, lineage registration, and domain stewardship.
  • Extend the governance toolchain beyond Data Hub by evaluating and deploying complementary platforms such as Collibra, Alation, or Atlan for policy management and business glossary;
    Monte Carlo or Lightup for data observability;
    One Trust or Privacera for privacy and entitlement governance; and Great Expectations or dbt tests for quality rule enforcement.
  • Own the enterprise Data Quality Framework, defining six quality dimensions that apply across all functions:
    Completeness, Accuracy, Timeliness, Consistency, Validity, and Uniqueness.
  • Classify every data asset into a quality tier:
    Tier 1 for business critical, Tier 2 for operational, Tier 3 for exploratory. SLAs and enforcement standards are documented, visible, and enforced by tier.
  • Formally document data quality SLAs per domain and per tier, publish them in Data Hub, and review them on a biannual cadence.
  • Define and publish a data health score methodology. Domain health scores are visible in the catalog and reported monthly to the data domain council.
  • Conduct quarterly data quality audits across all functions, share findings with the domain council, and assign remediation with named accountability and due dates.
  • Define and maintain the profiling policy: which datasets are profiled, how often, and what thresholds trigger an escalation. This is never left to individual interpretation.
  • Govern the separation between defining quality rules and implementing them. Governance defines. Engineering and Data Ops implement. When a breach…
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