Lead Data Quality Steward

Date:

1 Jul 2026

Location:

Petaling Jaya, MY

Company:

StarHub Ltd

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 :

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

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

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.

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

Responsibilities :

DQ Automation & Control Design

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.

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

Catalog & Trust Metadata Upkeep

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

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

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

Data Pattern Analysis & Issue Management

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

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

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

Stewardship Operating Model

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

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

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

Cross-functional Collaboration

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

Work with BI and business owners to align dataset expectations, certification requirements