EU remote
Middle Data Steward
About this role
Role purpose Act as the Data Steward for M&S Cockpit, serving as a key liaison between business stakeholders, data providers, and technical teams for one of the biggest food and beverages company - a long-temr customer of ours. The role focuses on understanding business requirements, coordinating data validation activities, ensuring data quality and consistency, and resolving discrepancies across sources, mappings, and reporting outputs.
It combines stakeholder management, business analysis, and data governance, ensuring that datasets are reliable, aligned with business expectations, and ready for downstream consumption. The Data Engineering team builds and maintains pipelines, while mappings and configurations determine cell/model scope; Snowflake extraction jobs run SQL through Databricks, with Blob Storage, validation, Medallion transformations, Power BI review, and Gold delivery to Ekimetrics.
The operating model also requires end-to-end triage across MDFA, CDF, WPP, Data Foundation, Redmill, and market stakeholders rather than treating every discrepancy as a coding defect. Key responsibilities Act as a Data Steward for the M&S Cockpit, ensuring data quality, consistency, completeness, and alignment with business requirements. Serve as a key point of contact for business stakeholders, including Product, CMIA/markets, WPP/MDFA, CDF, data providers, and Ekimetrics, to understand data needs, clarify expectations, and coordinate priorities.
Facilitate discussions with stakeholders to identify data issues, gather requirements, agree on definitions, and translate business needs into actionable tasks for technical teams. Coordinate data validation activities, ensuring that new data sources, market updates, and reporting outputs meet agreed business expectations. Monitor and investigate data-quality issues, including missing data, inconsistencies, anomalies, incorrect mappings, taxonomy differences, and reporting discrepancies.
Collaborate with Data Engineers and other technical teams to resolve data issues, improve data flows, and ensure reliable delivery of information. Maintain and improve data definitions, business rules, mapping documentation, and data governance standards across multiple sources and markets. Support stakeholder communication by providing regular updates on data quality, issue resolution, delivery readiness, and outstanding risks.