Lead Data Quality Steward
Confirmed on the employer's own hiring board on Oct 22, 2026. First seen by Alion on Oct 19, 2026.
About the role
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.
What you'll bring
Minimum Profile/ Track Record: 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. Core Traits (Non-negotiable) 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.