Role Scope

Data Steward is an individual contributor role responsible for executing core data stewardship activities that ensure the accuracy, integrity, and governance of enterprise data assets. The Data Steward manages end‑to‑end lineage monitoring, maintains high‑quality metadata, enforces policy and control compliance, and supports UAT for database changes and product distribution. Within the European Gas and Power Data team, the role supports the controlled transition of source data into internal databases and downstream delivery pipelines, ensuring raw data is validated, reconciled, transformed, and quality checked before it is made available to internal SMEs and external clients. The role performs detailed data validation, vendor data reviews, and works closely with Technology and Data Operations teams to resolve issues and maintain consistent data standards across systems.

Additionally, the Data Steward contributes to process improvement by driving standardization, enhancing documentation, and supporting the adoption of automation and AI‑enabled capabilities to improve data quality, reduce manual effort, and strengthen operational efficiency. We are looking for a process-orientated, methodological thinker with a keen eye for detail. Someone who enjoys dealing with large sets of data and problem solving.

What You'll Do (Key Responsibilities)

Operational Delivery

• Perform data validation checks, lineage tracking, and metadata updates to maintain data quality, integrity, and traceability.

• Execute data ingestion, validation, and integration activities in alignment with governance and control standards.

• Ensure consistent adherence to enterprise data policies and governance frameworks.

• Execute defined controls and compliance requirements to safeguard data integrity and lineage.

• Support vendor data reviews and onboarding through data quality assessments, validation checks, and issue resolution.

• Maintain data standardization, stewardship documentation, and process artifacts to ensure accuracy and consistency.

• Execute medium‑complexity stewardship and validation tasks under established guidelines, escalating exceptions when required.

• Respond to client data queries, resolving issues and coordinating issue triage with Operations and Client Services teams.

• Collaborate with Technology teams on defect resolution, enhancement requests, and UAT for backend data and dataflow changes.

• Contribute to automation and AI‑enabled improvements that enhance efficiency and strengthen stewardship capabilities.

• Investigate, validate, troubleshoot, and support controlled updates to transformation scripts, data quality rules, and pipeline logic using Python, SQL and PySpark, ensuring automated processes continue to run to schedule producing accurate outputs.

• Support onboarding of new datasets required by SMEs by reviewing source structures, validating expected outputs, identifying data quality risks, and coordinating required pipeline or source data changes within the existing architecture.

Process Excellence

• Support User Acceptance Testing (UAT) by validating data, coordinating test execution, and assisting with defect identification and resolution.

• Ensure adherence to governance, data management standards, and operating procedures by following defined controls and compliance requirements.

• Maintain high‑quality documentation by updating process flows, data definitions, data dictionaries, and other stewardship artifacts to ensure audit readiness and operational clarity.

Stakeholder Management

• Collaborate with Data Operations and Technology teams to support governance activities, quality controls, source amendments, validation outcomes and issue remediation while serving as a key liaison between governance expectations and day‑to‑day execution

• Support Product and Technology teams by assisting with data requirements, validations, and operational coordination throughout change cycles.

• Assist with vendor data reviews by conducting data quality checks, performing validations, and coordinating onboarding processes.

How Impact Will Be Measured (Success Metrics)

• Data accuracy and integrity - Maintain high accuracy rates through consistent validation, timely corrections, and adherence to defined quality thresholds.

• SLA and turnaround adherence - Meet or exceed service‑level timelines for data updates, validations, issue resolution, and stakeholder responses.

• Documentation completeness and currency - Keep process documentation, data dictionaries, lineage records, and stewardship artifacts up‑to‑date, audit‑ready, and fully aligned with governance standards.

• Pipeline quality and delivery - Ensure transformation scripts, database workflows, quality checks, and scheduled processes run successfully, with failures investigated, validated, resolved, or coordinated through to closure in line with operational priorities.

For This Role You'll Need to Have (Skills & Competencies)

Technical Skills

• Intermediate Python scripting skills, including the ability to investigate, maintain, debug, and support controlled updates to transformation logic, automation scripts, and data quality checks.

• Intermediate SQL capability to query, reconcile, transform, validate, and troubleshoot data across relational databases and pipeline outputs.

• Hands-on experience with PySpark or distributed data processing to support investigation, validation, reconciliation, and quality checks across large or complex datasets.

• Working knowledge of Databricks, including notebooks, jobs, clusters, and pipeline monitoring, would be advantageous.

• Understanding of cloud computing, preferably AWS.

• Working knowledge of AI/ML workflows.

• Works with metadata management and lineage tools to maintain data transparency and trace end‑to‑end data flows.

• Possess foundational familiarity with BI tools and dashboards to review outputs and identify data anomalies.

Functional Skills

• Possesses solid domain and process understanding

• Understands data governance fundamentals, including data quality controls, stewardship roles, and ownership models.

• Supports data standardization by applying consistent data definitions, formats, and controlled processes.

• Has working knowledge of data ingestion, transformation, validation, reconciliation, and integration workflows, with the ability to investigate pipeline failures, check source-to-target accuracy, support root cause resolution, and coordinate remediation actions.

• Collaborate with cross-functional teams to understand their data requirements/issues and generate innovative ideas and solutions.

Behavioral Skills

• Demonstrates strong attention to detail, ensuring accuracy in data, documentation, and validation activities,functional teams cause analysis.

• Communicates clearly and collaborates effectively with cross functional teams including Technology, Operations, Product, and Vendor partners.

• Appliesanalytical mindset, critical thinking & creative problem solving tobuild solutions for business problems and while investigating data issues and remediation throughroot cause analysis.