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.

· Solid 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.

📌 Data Quality & Governance Manager (Bengaluru)
🏢 Eleven
📍 Bengaluru