Data Quality & Governance Manager

Responsibilities:

·Build and lead a high-performing Data Quality team:hire, coach, and grow a team of DQ engineers, DQ analysts, and tech leads; own performance management and career development, and build succession depth by growing the next layer of DQ leaders within the GSC.

·Own the enterprise data quality strategy and roadmap:define the DQ vision, operating model, and multi-year roadmap for EDP, aligned with the Enterprise Data Governance Policy and Data Quality Policy and Standards, and evolve it as the data product portfolio scales.

·Architect the “fabric of protection”:design a layered, defense-in-depth DQ framework across the Bronze, Silver, and Gold layers of the lakehouse — shift-left validations in pipelines, circuit breakers that stop bad data from propagating, and continuous observability on data products in production.

·Drive proactive anomaly detection:implement rule-based and statistical monitors for freshness, volume, schema drift, distribution, and business-metric anomalies to catch issues before they reach dashboards, stores, or partners, with tuned thresholds to minimize fatigue.

·Build strong reactive capabilities:establish a DQ incident management process covering triage, severity classification (P0–P3), on-call, impact assessment through lineage, root-cause analysis, remediation SLAs, and blameless post-mortems, with measurable reductions in MTTD and MTTR.

·Live and speak the language of data:go deep into datasets — profiling, reconciliation, and understanding the business semantics of sales, transactions, loyalty, inventory, and supply chain data — to identify recurring trends, systemic weak points, and root causes across domains, and convert them into preventive controls.

·Operationalize data governance:translate data governance and data quality policies and standards into enforceable, automated controls — data ownership and stewardship models, metadata and lineage completeness, business glossary alignment, data classification (PII / PCI), and audit-ready evidence of quality.

·Institutionalize quality expectations for data products:partner with data product owners and stewards to define critical data elements (CDEs), data contracts, quality SLAs/SLOs, and governed / certified asset criteria so that every certified data product has explicit, measurable quality expectations and a named owner.

·Own the DQ platform and tooling:evolve our enterprise data observability platform, Databricks / Unity Catalogue native quality checks, metadata-driven rules frameworks, reusable DQ libraries, and the self-service DQ portal, driving automation, reuse, and adoption across engineering teams.

·Embed data quality as code:ensure DQ rules are versioned in Git, automated checks run in CI/CD, pipelines have test automation, and quality gates block the promotion of non-conforming data or code.

·Measure and make quality visible:define and publish DQ KPIs and scorecards — dimension scores (accuracy, completeness, consistency, timeliness, validity, uniqueness), domain health scores, incident trends, and SLA adherence — and surface quality at the point of use through the data catalogue and BI dashboards.

·Be the voice of data trust:run DQ and governance forums with data owners and stewards, partner with stakeholders across the organization, and brief senior leadership on DQ posture, risks, and business impact.

·Champion a “quality is everyone's job” culture:role-model the 7-Eleven Leadership Principles, drive DQ accountability across producing and consuming teams, and bring outside-in industry best practices.

Educational & Required Qualifications:

·10–14 years of experience in data quality, data governance with at least 5–8 years leading data quality teams is a must

·Proven track record of building and scaling high-performing data quality teams and programs at enterprise scale — able to show how the DQ program measurably improved data trust and business outcomes.

·Deep, hands-on expertise in data quality dimensions, data profiling, rule design, reconciliation, anomaly detection, and root-cause analysis across large, complex, multi-source datasets.

·Strong grounding in data governance — data ownership and stewardship, metadata and lineage, data catalogues (e.g., Alation, Collibra), business glossaries, data contracts, CDEs, and data classification; awareness of privacy and compliance requirements (e.g., PCI, PII).

·Strong hands-on SQL and Python / PySpark skills — able to dig into the data personally to diagnose issues and validate fixes.

·Hands-on experience with DQ / observability tools — e.g., Monte Carlo, Great Expectations, Soda, Deequ / DQX, Informatica DQ.

·Solid understanding of the modern data stack on cloud (Azure / AWS / GCP) — Databricks, Delta Lake, Spark, Kafka, Airflow, dbt, and medallion / lakehouse architectures.

·Working knowledge of statistical anomaly detection — time-series baselining, seasonality, and outliers.

·Experience establishing data incident management practices — severity models, SLAs, on-call, RCA, and ticketing integration (JIRA / ServiceNow).

·Strong SDLC rigor across CI/CD, version control (Git / GitLab), test automation, and DataOps practices.

·AI-savvy — pragmatic about using AI-assisted tools to accelerate data quality and governance work.

·Excellent communication skills — able to translate data issues into business impact, brief senior stakeholders, and influence across a matrixed global organization.

·Prior experience in retail, e-commerce, CPG, or convenience-retail organizations strongly preferred — with exposure to sales, transaction, loyalty, merchandising, supply chain, or store operations data.

·Bachelor's degree in Computer Science, Engineering, Statistics, or a related field; advanced degree preferred. Certifications such as DAMA CDMP or Databricks are a plus.