
Data Governance Lead
Supermetrics · Dublin, County Dublin, Ireland
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- Hybrid
- Full-time
- €100,000 / year
- Dublin, County Dublin, Ireland
Job highlights
- Design and own data governance framework.
- Lead data catalog implementation and operation.
- Establish data stewardship program.
- Define data quality standards and SLAs.
- Contribute to AI governance initiatives.
About the role
About the Role
Data is our entire value proposition—join us to build out the governance structures to support the next generation of intelligence at Supermetrics. We’re looking for a Data Governance Lead to join our Data Strategy team in Helsinki or Dublin office. In this role, you will…- Design and own Supermetrics’ data governance framework — defining the standards, policies, and practices that govern how data is created, classified, and maintained across the business.
- Lead the implementation and ongoing operation of our data catalogue (Open Metadata), ensuring every business-critical data asset is documented and discoverable.
- Establish and manage a data stewardship programme — partnering with business domain leads to define data ownership and accountability.
- Define data quality standards and SLA’s, working with data owners to improve the overall quality of data consumed, integrated and presented to the wider business.
- Contribute to our AI governance agenda — ensuring that AI workflows and models built on our data are grounded in governed, auditable, and well-documented foundations.
- Framework Design: Defining the policies and standards for data creation, classification, and maintenance across the business.
- Catalog Management: Leading the evolution of our data catalogue (Open Metadata) to ensure assets are documented and well-understood.
- Data Stewardship: Partnering with business domain leads to define data ownership and ensuring those accountabilities are actively maintained.
- Quality & SLAs: Working hands-on with the Data Platform team to instrument automated quality monitoring across our pipelines (BigQuery, dbt, Elementary).
- Compliance Partnership: Partnering with Legal (GDPR) and IT (SOC2) to ensure data handling, retention, and access policies are compliant and audit-ready.
- Clarity & Literacy: Building a company-wide data dictionary and business glossary while advocating data literacy and governance culture.
- Leadership: Establishing and chairing a Data Governance Working Group with cross-functional representation.
- 6+ years in data roles, with demonstrable experience designing or implementing data governance frameworks in a cloud-native environment.
- Proven hands-on experience with data cataloguing and metadata management tools (Open Metadata, Collibra, Alation, DataHub, or equivalent).
- Solid working knowledge of GDPR and data compliance obligations in a SaaS or technology company context.
- Familiarity with modern data stack components: cloud data warehousing (BigQuery preferred), data transformation (dbt), and pipeline orchestration.
- Excellent stakeholder management and communication skills — equally comfortable engaging with engineering teams and senior business leaders.
- Structured, process-oriented approach — able to translate governance principles into practical, enforceable operational standards.
- Experience in a B2B SaaS or high-growth technology environment.
- Hands-on experience with data quality tooling such as Elementary, Great Expectations, or Monte Carlo.
- Familiarity with AI and ML governance concepts — model lineage, responsible AI frameworks, or bias detection.
- CDMP, DAMA, or equivalent data governance certification.
- Experience with Snowplow, Airflow, or event-driven data architectures.
- Competitive compensation package, including equity
- Great work equipment
- Health care benefit and leisure time insurance
- Annual 1000 euros of personal learning budget
- Sports and wellbeing allowance
- Data Governance
- Data Strategy
- Data Catalog
- Metadata Management
- Data Stewardship
- Data Quality
- GDPR
- SaaS
- BigQuery
- dbt
Skills & topics
- Data Governance Lead
- Data Governance
- Data Strategy
- Data Catalog
- Metadata Management
- Data Stewardship
- Data Quality
- GDPR
- SaaS
- BigQuery
- dbt
- Open Metadata
- Helsinki
- Dublin
- Data Management
How to get hired
- Tailor your resume: Highlight your 6+ years in data roles and experience with data governance frameworks, data cataloging tools (like Open Metadata), and cloud-native environments.
- Showcase relevant skills: Emphasize your knowledge of GDPR, modern data stack components (BigQuery, dbt), and stakeholder management.
- Address the 'appreciated' skills: If applicable, mention experience in B2B SaaS, data quality tooling, AI/ML governance, or certifications like CDMP.
- Craft a compelling cover letter: Clearly articulate why your structured, process-oriented approach makes you the ideal Data Governance Lead for Supermetrics' mission.
- Prepare for interviews: Be ready to discuss your experience designing governance frameworks and implementing data catalog solutions.
Technical preparation
Master data cataloging tools (Open Metadata preferred).,Understand data warehousing (BigQuery) and transformation (dbt).,Familiarize yourself with data quality tooling.,Review GDPR and SaaS compliance obligations.
Behavioral questions
Describe designing a data governance framework.,How do you manage stakeholders in data projects?,Explain your approach to data stewardship.,How would you foster a data governance culture?
Frequently asked questions
- What are the main responsibilities of a Data Governance Lead at Supermetrics?
- As a Data Governance Lead at Supermetrics, your primary responsibilities include designing and owning the company's data governance framework, leading the implementation and operation of the data catalog (Open Metadata), establishing a data stewardship program, defining data quality standards, and contributing to the AI governance agenda.
- What specific data tools and technologies are important for this Data Governance Lead role?
- The role requires hands-on experience with data cataloguing and metadata management tools such as Open Metadata, Collibra, or Alation. Familiarity with modern data stack components like BigQuery, dbt, and pipeline orchestration is also essential. Experience with data quality tools like Elementary is a plus.
- Does Supermetrics require specific certifications for the Data Governance Lead position?
- While not strictly required, Supermetrics appreciates candidates with certifications such as CDMP, DAMA, or equivalent data governance certifications. The focus is more on demonstrated experience and practical application of data governance principles.
- What is the expected experience level for the Data Governance Lead role at Supermetrics?
- Supermetrics is looking for candidates with at least 6 years of experience in data roles, specifically with demonstrable experience in designing or implementing data governance frameworks in a cloud-native environment.
- How does Supermetrics approach data governance and AI governance?
- Supermetrics views data as its core value proposition. The company is building out governance structures to support its data strategy and intelligence initiatives. This includes a focus on AI governance, ensuring AI workflows and models are grounded in governed, auditable, and well-documented foundations.
- What are the key benefits offered to employees at Supermetrics?
- Supermetrics offers a competitive compensation package including equity, great work equipment, health care and leisure time insurance, an annual personal learning budget of 1000 euros, and a sports and wellbeing allowance. Benefits may vary by location.
- Is this Data Governance Lead position remote, hybrid, or on-site?
- The Data Governance Lead role is based in either the Helsinki or Dublin office, indicating an on-site or potentially hybrid work arrangement depending on specific team policies and location.
- What kind of data quality initiatives will the Data Governance Lead be involved in?
- The Data Governance Lead will define data quality standards and Service Level Agreements (SLAs). They will work with data owners to improve data quality and collaborate with the Data Platform team to implement automated quality monitoring across data pipelines using tools like BigQuery, dbt, and Elementary.