Role Mission:

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. 

Accountabilities:

1. Own the data stewardship execution model for priority C360 datasets, including ownership, trust status, certification criteria, and quality controls.  

2. Design, implement & improve automated data quality rules, checks, and remediation workflows for governed datasets; include leveraging AI agents where possible.  

3. Maintain data catalog hygiene and trust metadata so critical datasets are discoverable, understandable, and certified for use.  

4. Analyse data patterns, exceptions, and recurring defects to identify root causes, quality trends, and control gaps.  

5. Partner with Data Engineering, Platform Engineering, Architecture & Governance, BI, and business owners to translate business definitions into executable quality and trust controls.  

6. Lead stewardship operations across domains by setting standards, coaching stewards, and driving consistent issue handling and escalation.  

7. Improve data trust & literacy maturity through practical mechanisms, not manual follow-up or documentation alone. 

Responsibilities:

1. DQ Automation & Control Design  

1. Define, implement and improve automated checks for completeness, uniqueness, validity, referential integrity, reconciliation, freshness, and schema drift.  

2. Use SQL, Python, Snowflake, orchestration tooling, and metadata workflows to operationalize DQ checks and issue detection.  

3. Build reusable patterns for DQ checks, thresholding, alerting, and exception handling across domains.  

2. Catalog & Trust Metadata Upkeep  

1. Maintain data catalog entries, glossary alignment, ownership metadata, business definitions, and certification status for priority datasets.  

2. Ensure critical datasets have clear descriptions, intended usage, trust state, and stewardship ownership.  

3. Work with architects and data engineers to keep lineage notes, business terms, and technical metadata consistent.  

3. Data Pattern Analysis & Issue Management  

1. Analyse recurring data defects, pattern shifts, anomalies, and control failures to identify whether issues originate from source, pipeline, or business-definition gaps.  

2. Triage DQ incidents, prioritize by business impact, and route to the right owner with clear evidence and expected action.  

3. Convert repeated manual interventions into durable rules, controls, or monitoring mechanisms.  

4. Stewardship Operating Model  

1. Run the stewardship workflow for owned domains: review, decision, remediation, escalation, certification, and closure.  

2. Define and maintain operating standards for issue classification, trust-state updates, exception handling, and sign-off criteria.  

3. Support senior stewardship planning and cross-domain consistency without becoming a paperwork-only role.  

5. Cross-functional Collaboration  

1. Partner closely with Data Engineering to embed DQ checks into pipelines and curated layers, rather than bolting them on after delivery.  

2. Work with BI and business owners to align dataset expectations, certification requirements, and acceptable-use boundaries.  

3. Collaborate with Platform Engineering on metadata plumbing, access control interpretation, audit evidence, and enablement tooling.  

6. Enablement & Coaching  

1. Coach and enable domain data users on how to use data, interpret controls, interpret scorecards, and manage data trust issues.  

2. Produce practical playbooks, templates, and decision logs that improve consistency and reduce dependency on manual memory.  

3. Use AI-assisted summarization, classification, and knowledge retrieval to reduce stewardship toil, with human review for trust or production-impacting decisions. 

Team Scope/ Stakeholders: 

• Scope: Priority C360 datasets, stewardship workflows, quality rules, catalog metadata, certification status, and trust controls across the DXP Data Platform.  

• Decision Rights: DQ rule design within stewardship domain, trust-state recommendation, issue routing and severity classification, certification workflow design, and prioritization of stewardship actions.  

• Stakeholders: Data Engineering, Platform Engineering, Architecture & Governance, BI, business data owners, source-system owners, analysts, and domain stewards.  

• Not in Scope: This role does not own downstream BI dashboards, does not own full source-system remediation, and does not replace Data Engineering for heavy pipeline build work. 

Minimum Profile/ Track Record: 

• 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.  

• Comfortable using AI-enabled workflows to reduce manual triage and accelerate analysis, while preserving auditability and human accountability. 

Core Traits (Non-negotiables) 

• 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.