Data Governance Manager

TQL•Cincinnati, OH

38d•Onsite

About The Position

The Data Governance Manager will design and operationalize our data governance program — with a specific focus on data quality, certified data products, data and API cataloging, metadata management, and knowledge graphs. This is not a policy-and-compliance role sitting apart from the business; it's a hands-on, relationship-driven role embedded with operations, technology, and commercial teams to ensure data is discoverable, understood, trusted, and used to drive better freight decisions. Success here is measured less by frameworks written and more by adoption earned — stakeholders choosing certified data because they trust it and trust you.

Requirements

7+ years in data governance, data management, or data quality roles, ideally in logistics, freight, supply chain, or another high-transaction-volume industry
Direct experience building or scaling data catalogs, metadata repositories, data product frameworks, or knowledge graphs (e.g., Collibra, Alation, Atlan, Informatica, Purview, Neo4j, or equivalent), with hands-on exposure to modern data platforms such as Databricks and Azure
Working knowledge of API management and data architecture concepts — enough to partner credibly with engineering
A track record of building trust and driving adoption in organizations without heavy top-down mandate — you lead through relationships and results, not authority
Comfort operating in a fast, informal, founder-led culture: pragmatic, biased to action, allergic to needless bureaucracy
Excellent communication skills — equally credible in a room with data engineers and in a room with brokers or ops leaders
Bachelor's degree in a related field (Business, Information Systems, Data/Analytics, Supply Chain) or equivalent experience

Responsibilities

Define and enforce data quality standards, monitoring, and remediation workflows across core domains (loads, carriers, customers, pricing, tracking/visibility events). Score and communicate Data Quality enhancements to drive one or more of revenue engine, margin engine, or capital efficiency engine
Establish a data product certification framework — clear ownership, SLAs, quality thresholds, and documentation — so teams can self-serve trusted data with confidence
Build and maintain a living catalog of data assets and internal/external APIs (TMS, carrier integrations, load boards, customer portals) so teams can find, understand, and reuse data instead of re-building it
Implement business glossaries, lineage tracking, and metadata standards that make data self-explanatory to both technical and non-technical users
Partner with data/engineering teams to model relationships across shipments, carriers, customers, and lanes into a knowledge graph that powers search, recommendations, and operational insight
Act as the connective tissue between data engineering, operations, sales, and finance — running working sessions, resolving competing definitions, and building genuine trust so governance is seen as an enabler, not a gatekeeper
Stand up lightweight, pragmatic governance councils, escalation paths, and decision rights appropriate for a fast-moving, founder-led environment
Drive adoption through training, documentation, and visible wins rather than mandates alone