Lead Data Steward

About the role

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.

Key responsibilities

• Own the data stewardship execution model for priority C360 datasets, including ownership, trust status, certification criteria, and quality controls
• Design, implement and improve automated data quality rules, checks, and remediation workflows for governed datasets
• Maintain data catalog hygiene and trust metadata so critical datasets are discoverable, understandable, and certified for use
• Analyse data patterns, exceptions, and recurring defects to identify root causes, quality trends, and control gaps
• Partner with Data Engineering, Platform Engineering, Architecture & Governance, BI, and business owners to translate business definitions into executable quality and trust controls
• Lead stewardship operations across domains by setting standards, coaching stewards, and driving consistent issue handling and escalation
• 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
• Analyse recurring data defects, pattern shifts, anomalies, and control failures to identify whether issues originate from source, pipeline, or business-definition gaps
• Coach and enable domain data users on how to use data, interpret controls, interpret scorecards, and manage data trust issues

About you

• 5–9+ 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
• Experience partnering with engineers to embed quality checks into pipelines and to avoid manual recurring remediation
• Strong stakeholder handling: can translate business meaning into technical controls without becoming vague, overly administrative, or over-promising
• Comfort working with metadata tools, dashboards, ticketing workflows, and operational evidence
• 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