The RoleEDO is seeking a Data Product Operations Manager to ensure the integrity, consistency, and scalability of our data products. This role will own the governance, taxonomy, documentation, and operational processes that underpin how our data is defined, managed, and consumed across the organization. As our data ecosystem continues to grow in complexity, this individual will serve as the steward of our business definitions, metrics, entities, dimensions, and metadata. They will ensure that data consumers-from Product and Engineering to Data Science, Analytics, and Client Services-have a clear and consistent understanding of the data that powers our products and insights.
This is a highly hands-on role. The ideal candidate enjoys diving into data models, pipeline logic, business definitions, and documentation while also building the processes and governance mechanisms that keep them accurate over time.
What You Will DoOwn Data Taxonomy & Business Definitions- Develop and maintain the company's data taxonomy, including business entities, metrics, dimensions, classifications, and naming conventions.
- Establish authoritative definitions for key business concepts and data products.
- Ensure consistent usage of definitions across reporting, analytics, applications, and customer-facing products.
- Manage the lifecycle of data definitions, including creation, modification, deprecation, and communication of changes.
Drive Data Product Governance- Establish processes for reviewing, approving, and documenting changes to metrics, calculations, and business logic.
- Partner with Product, Data Science, and Engineering teams to ensure definitions remain aligned as products and pipelines evolve.
- Resolve ambiguities and inconsistencies in business terminology and data interpretation.
- Create governance standards that balance consistency with the need for rapid innovation.
Maintain Metadata & Documentation- Build and maintain comprehensive data dictionaries, business glossaries, and metadata repositories.
- Document data lineage, transformation logic, calculation methodologies, and business rules.
- Ensure documentation stays synchronized with changes to data pipelines, models, and applications.
- Identify and close gaps in documentation and metadata coverage.
Improve Data Product Operations- Create operational processes that improve trust, discoverability, and usability of data assets.
- Define standards for data quality, documentation completeness, and metadata management.
- Partner with Engineering teams to automate metadata collection, lineage tracking, and governance workflows where possible.
- Monitor and report on the health and maturity of the organization's data management practices.
Recurring Delivery Operations- Own the operational cadence for data products delivered on a continuous, scheduled basis-ensuring every recurring deliverable is produced reliably, accurately, and on time.
- Maintain a clear picture of all active delivery workstreams, deadlines, and cross-team dependencies; surface risks and blockers early before they affect clients.
- Coordinate across Engineering, Data Science, Analytics, and Client Services to keep delivery workflows moving without gaps or handoff failures.
- Build and maintain internal delivery trackers and status dashboards that give the team clear visibility into what's due, what's at risk, and what's been shipped.
Client Delivery Support & Relationship Management- Serve as the primary operational point of contact for clients with active, subscription-based data relationships-receiving issue reports and translating them into clear internal action items.
- Track internal progress against client delivery commitments and proactively flag when timelines are at risk, escalating through the right channels to protect the client relationship.
- When internal capacity or complexity requires more time, negotiate adjusted timelines with clients in a way that preserves trust while creating realistic space for the team to deliver.
- Maintain ongoing communication with clients to provide status updates and set accurate expectations-so clients always know where things stand without having to chase for answers.
Operational Improvement Advocacy- Identify and document recurring friction points in the delivery process-whether rooted in manual workflows, tooling gaps, unclear ownership, or process breakdowns.
- Translate operational pain into structured, evidence-backed proposals for Engineering and Product teams, making the case for investments in tooling, automation, or process redesign.
- Work closely with Engineering and Product to prioritize and implement improvements that meaningfully reduce delivery risk and increase team throughput over time.
- Represent the needs of delivery operations in cross-functional planning discussions, ensuring operational capacity is factored into product and engineering roadmaps.
Cross-Functional Leadership- Act as the connective tissue between Product, Engineering, Data Science, Analytics, and Business stakeholders.
- Translate technical implementations into clear business-facing definitions and documentation.
- Drive adoption of governance standards through education, training, and stakeholder engagement.
- Serve as the source of truth for questions related to metric definitions, data lineage, and business terminology.
What We Are Looking For- 5+ years of experience in data governance, analytics engineering, data management, business intelligence, data architecture, or related fields.
- Experience creating and maintaining business glossaries, metric catalogs, taxonomies, or metadata management programs.
- Strong understanding of data warehousing, ETL/ELT pipelines, dimensional modeling, and analytics workflows.
- Ability to understand and document complex data transformations and business logic.
- Excellent written communication and documentation skills.
- Strong attention to detail and passion for creating clarity from complexity.
- Familiarity with data governance, lineage, and metadata management best practices.
- Track record operating in a fast-growing data-driven technology company.
- Experience with Snowflake, dbt, or modern cloud data platforms.
- Experience with data catalog and metadata management tools such as Atlan, Alation, Collibra, DataHub, or similar platforms.
Compensation & BenefitsEDO offers a competitive compensation package. Components of compensation include:
- Mid-stage equity and competitive salary
- Flexible Time Off
- Medical, dental and vision coverage: EDO provides full coverage for individual medical plans and partial coverage for dependent or family plans.
- 401(k) plan, FSA, HSA
- Commuter Benefits
- When in an office, employee meals, snacks, and more
In compliance with New York Pay Transparency Law, the salary range for this position is $110,000-$130,000. We note the salary information as a general guideline only, as actual compensation may vary from posting. We will consider various factors to determine the offer for this role, including the scope and responsibilities of the position, relevant work experience, location, key skills, training, and business considerations.
