Lead Data Quality Steward

• To lead DXP C360 Data Platform data quality stewardship execution by combining stewardship, analytical, and practical engineering skills to make trusted data measurable, operational, understandable and sustainable.
• This role owns the design and upkeep of data quality controls, catalog integrity, trust metadata, and issue-remediation workflows for priority datasets, ensuring business users can rely on governed data products for self-service analytics.
• This is a hands-on stewardship role with engineering depth: it is expected to implement DQ automation, analyse data patterns, maintain catalog and trust metadata, and partner with Data Engineering to embed controls into production flows. It is not a pure policy or governance coordinator role.

Responsibilities

• Define, implement and improve automated checks for completeness, uniqueness, validity, referential integrity, reconciliation, freshness, and schema drift.
• Use SQL, Python, Snowflake, orchestration tooling, and metadata workflows to operationalize DQ checks and issue detection.
• Maintain data catalog entries, glossary alignment, ownership metadata, business definitions, and certification status for priority datasets.
• Analyse recurring data defects, pattern shifts, anomalies, and control failures to identify whether issues originate from source, pipeline, or business-definition gaps.
• Partner closely with Data Engineering to embed DQ checks into pipelines and curated layers, rather than bolting them on after delivery.
• Coach and enable domain data users on how to use data, interpret controls, interpret scorecards, and manage data trust issues.
• Scope: Priority C360 datasets, stewardship workflows, quality rules, catalog metadata, certification status, and trust controls across the DXP Data Platform.

• 4-6 years of experience in data stewardship, data quality, analytics operations, data governance with technical depth, or adjacent data engineering work.
• Strong ability to work with Snowflake SQL and Python, and to use them for profiling, rule implementation, exception analysis, and control automation.
• Demonstrated experience implementing or operating DQ frameworks, scorecards, certification workflows, or metadata/catalog processes.
• Strong practical understanding of how data is modeled, moved, transformed, and checked in cloud data platforms such as Snowflake, Airflow, Airbyte, and AWS.
• Ability to analyse data distributions, data drift, exception patterns, and defect recurrence to identify meaningful control improvements.
• Strong stakeholder handling: can translate business meaning into technical controls without becoming vague, overly administrative, or over-promising.

• Engineering-minded stewardship: able to implement controls, not just define them.  
• Trust-first mindset: focuses on whether data can be relied on, not just whether a rule exists.  
• Pattern-based problem solving: looks for repeatable causes and converts repeated issues into mechanisms.  
• Collaborative discipline: works with engineers and business owners without over-escalating or hand-waving.  
• Production awareness: understands that stewardship exists to protect operational data use, not to create bureaucracy.