Data Quality Manager - SQL/Python
First seen by Alion on Nov 12, 2026.
About the Role :
We are looking for a seasoned Data Quality Manager to build and lead a dedicated Data Quality team within 7-Eleven's Enterprise Data Platform (EDP) organization at our Global Solution Centre. Trusted, well-governed data is the foundation of everything we do - from store operations and executive reporting to customer insights, merchandising, and supply chain decisions.
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 the enterprise data quality strategy and roadmap: define the DQ vision, operating model, and multi-year roadmap for EDP.
- Architect the "fabric of protection": design a layered, defense-in-depth DQ framework across the Bronze, Silver, and Gold layers of the lakehouse.
- Drive proactive anomaly detection: implement rule-based and statistical monitors for freshness, volume, schema drift, distribution, and business-metric anomalies.
- Build strong reactive capabilities: establish a DQ incident management process covering triage, severity classification, on-call, and root-cause analysis.
- Live and speak the language of data: go deep into datasets to identify recurring trends, systemic weak points, and root causes.
- Operationalize data governance: translate data governance and data quality policies and standards into enforceable, automated controls.
- Institutionalize quality expectations for data products: partner with data product owners and stewards to define critical data elements (CDEs), data contracts, and quality SLAs/SLOs.
- Own the DQ platform and tooling: evolve our enterprise data observability platform, metadata-driven rules frameworks, and self-service DQ portal.
- Embed data quality as code: ensure DQ rules are versioned in Git, automated checks run in CI/CD, and pipelines have test automation.
- Measure and make quality visible: define and publish DQ KPIs and scorecards.
- Be the voice of data trust: run DQ and governance forums and brief senior leadership on DQ posture.
- Champion a "quality is everyone's job" culture: role-model the 7-Eleven Leadership Principles.
Required Qualifications :
- 10 - 14 years of experience in data quality, data governance with at least 5 - 8 years leading data quality teams.
- Proven track record of building and scaling high-performing data quality teams.
- Deep, hands-on expertise in data quality dimensions, data profiling, rule design, reconciliation, anomaly detection, and root-cause analysis.
- Strong grounding in data governance: data ownership, metadata and lineage, data catalogues (e.g., Alation, Collibra), business glossaries, data contracts, and data classification.
- Strong hands-on SQL and Python / PySpark skills.
- 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.
- Experience establishing data incident management practices.
- Strong SDLC rigor across CI/CD, version control (Git / GitLab), test automation, and DataOps practices.
- Excellent communication skills.
- Prior experience in retail, e-commerce, CPG, or convenience-retail organizations strongly preferred.
- Bachelor's degree in Computer Science, Engineering, Statistics, or a related field; advanced degree preferred.
Skills
Data Quality, Data Governance, SQL, Python, PySpark, Data Profiling, Collibra, AWS, Azure, Google Cloud Platform, DataOps