Senior Manager / Director, Data Quality & Trust

Career Guide
A Senior Manager/Director of Data Quality & Trust leads the strategy, teams, and cross‑company programs that ensure data is accurate, consistent, secure, and reliably used for decision‑making and customer-facing products. The role typically owns data quality standards, measurement, monitoring, issue remediation, governance, and stakeholder trust across analytics, AI/ML, reporting, and operational systems.

Key Responsibilities

  • Set the enterprise data quality and trust strategy (standards, policies, success measures, operating model).
  • Define what “good data” means for key domains (customers, products, finance, risk) and create clear rules and definitions.
  • Build and run a data quality program: profiling, controls, automated checks, monitoring dashboards, and alerting.
  • Lead incident response for data issues (triage, root-cause analysis, fixes, prevention, communications).
  • Partner with Engineering, Data Platform, Analytics, Product, Security, and Compliance to bake quality into pipelines and products.
  • Establish data governance practices: data ownership, stewardship, approvals, metadata/documentation, and change management.
  • Oversee data lineage and transparency so teams can understand where data came from and how it’s transformed.
  • Create audit-ready evidence for quality controls and regulatory requirements where applicable.
  • Manage and mentor managers/ICs (data quality engineers, analysts, governance leads), including hiring and performance development.
  • Communicate tradeoffs and ROI to executives (risk reduction, improved decision quality, fewer incidents, faster delivery).

Top Skills for Success

Program leadership (multi-team planning, prioritization, and delivery)
Stakeholder management and influence with executives and senior partners
Clear communication (turning complex data issues into business impact and action plans)
Data quality frameworks and measurement (rules, thresholds, scorecards, SLAs/SLOs)
Root-cause analysis for data issues across pipelines, systems, and processes
Data governance fundamentals (ownership, stewardship, policies, approvals, documentation)
Data engineering literacy (ETL/ELT concepts, orchestration, data modeling basics, SQL)
Metadata, lineage, and documentation practices to improve transparency and trust
Risk, privacy, and compliance awareness (data handling expectations and controls)
Vendor/tool evaluation and implementation (data quality, observability, catalog tools)

Career Progression

Can Lead To
Director/VP of Data Governance
Director/VP of Data Platform or Data Engineering
Head of Data Management / Master Data Management (MDM)
Chief Data Officer (in some organizations)
Transition Opportunities
Director/Head of Analytics Engineering
Director of Risk & Controls (data-focused)
Product leadership for data platforms (Data Product Director)
AI governance and model risk leadership (where AI is core to the business)

Common Skill Gaps

Often Missing Skills
Quantifying business impact of poor data quality (lost revenue, cost, risk exposure) with clear metricsBuilding automated monitoring and alerting instead of manual checksDefining ownership and escalation paths so issues get fixed quicklyBalancing speed vs. control when teams ship new data productsCreating executive-ready reporting (simple, trusted scorecards and trends)Experience with regulatory expectations (varies by industry) and audit evidence
Development SuggestionsBuild a small, measurable pilot program around one high-value data domain: define quality rules, implement automated checks, create a weekly scorecard, and set a clear incident workflow. Use before/after results (fewer incidents, faster resolution, improved KPI trust) to prove value and earn broader adoption.

Salary & Demand

Median Salary Range
Entry LevelTypically not an entry-level role; most hires require 8–12+ years of experience.
Mid LevelUS: ~$170k–$230k base (often plus bonus/equity, depending on company size and location).
Senior LevelUS: ~$230k–$320k+ base (often plus bonus/equity; top tech/finance can exceed this range).
Growth Trend
Strong and growing demand, driven by AI adoption, increased regulatory scrutiny, modernization of data platforms, and higher expectations for trustworthy metrics and reporting. Many organizations are formalizing data quality programs that used to be ad hoc.

Companies Hiring

Major Employers
Large technology companies with data platforms and AI productsFinancial services firms (banks, insurers, payment networks)Healthcare and life sciences organizationsRetail and e-commerce companies with large customer and supply-chain dataEnterprise software and SaaS companiesTelecom and utilitiesConsulting and systems integrators building governance/quality programs for clients
Industry Sectors
TechnologyFinancial ServicesHealthcareRetail & E-commerceManufacturing & Supply ChainTelecommunicationsGovernment and regulated industries

Recommended Next Steps

1
Create a 30–60–90 day plan template: key stakeholders, top data domains, current pain points, and quick-win controls.
2
Develop a repeatable “data incident” playbook (severity levels, owners, communications, and prevention steps).
3
Build a simple quality scorecard for 3–5 critical metrics (accuracy, completeness, timeliness, consistency, uniqueness) and define targets.
4
Strengthen hands-on capability in SQL and data pipeline concepts to partner effectively with engineering teams.
5
Prepare 2–3 leadership stories for interviews: a major data issue you resolved, a program you scaled, and a governance change you drove.
6
Map tools used in your target companies (quality checks, monitoring, catalog/lineage) and be ready to discuss evaluation criteria and rollout approach.
7
Network with adjacent leaders (Data Platform, Security/Privacy, Finance/Risk) to understand how trust is measured and funded in your industry.