Role Overview
We are seeking an experienced Technical Data Steward with strong hands-on experience in data analysis, data quality, data profiling, metadata, lineage, and supply chain data. The role will work closely with Product Managers, Business SMEs, Data Engineers, Data Architects, and Analytics teams to ensure MedTech Supply Chain data is accurate, consistent, traceable, and fit for business consumption.
Key Responsibilities
- Analyze large and complex MedTech Supply Chain datasets across multiple source systems.
- Perform source-to-target validation, data reconciliation, profiling, and root-cause analysis.
- Define and implement data quality rules covering completeness, accuracy, consistency, uniqueness, and timeliness.
- Understand source data, transformation logic, curated data, and downstream consumption.
- Define and maintain source-to-target mappings and business transformation rules.
- Validate data transformations implemented by Data Engineering teams.
- Maintain business and technical metadata and document end-to-end data lineage.
- Identify Critical Data Elements and define appropriate validation rules.
- Investigate production data issues across source systems, mappings, transformations, master data, and downstream logic.
- Translate business data-quality requirements into automated technical validations.
- Perform impact analysis for schema, mapping, source-system, and business-rule changes.
- Support metadata and lineage maintenance within enterprise data catalog platforms.
Technical Skills
- Advanced SQL: complex joins, CTEs, window functions, aggregations, reconciliation, duplicate detection, validation, and root cause analysis.
- Strong hands-on experience with Data Profiling, Data Quality, Data Validation, Data Reconciliation, Source-to-Target Mapping, Data Lineage, Metadata Management, Master Data, Reference Data, and Data Modeling concepts.
- Ability to independently analyze millions of records and identify patterns, anomalies, and data-quality issues.
Preferred Technology Experience
- Python / PySpark
- Databricks
- Azure Data Platform
- Delta Lake / Lakehouse
- SAP data analysis
- Power BI or similar analytics tools
- Alation / Collibra / Microsoft Purview or similar data catalog and governance tools
- Git / version-controlled data artifacts
- Automated data-quality frameworks
Supply Chain Data Knowledge
- Material / Product
- Plant
- Storage Location
- Sales Orders
- Purchase Orders
- Inventory
- Deliveries
- Shipments
- Customers
- Suppliers / Vendors
- Manufacturing / Production Orders
- Material Movements
- Demand Planning
- Supply Planning
- ATP / Available-to-Promise
- Lead Times
- Backorders
- Order Holds / Blocks
- Fulfillment
- OTIF / Delivery Performance
Data Governance & Stewardship
Understanding of Data Ownership, Business Glossary, Critical Data Elements, Metadata, Data Classification, Data Lineage, Data Quality Rules, Data Standards, Authoritative Data Sources, and Business/Technical Definitions. This is a hands-on technical role requiring the ability to query, profile, validate, and troubleshoot underlying data—not a purely governance or documentation role.
Required Experience
- 7+ years of overall Data / Analytics / Data Engineering / Data Governance experience.
- 3+ years in Data Stewardship, Data Quality, Data Analysis, Data Governance, or a closely related technical data role.
- Strong hands-on SQL experience.
- Experience with large enterprise datasets and multiple source systems.
- Experience with data profiling, reconciliation, data-quality validation, and root-cause analysis.
- Experience understanding data models and relationships between enterprise business entities.
- Experience working directly with Data Engineering and Data Architecture teams.
- Experience translating business rules into technical data-validation rules.
- Strong analytical and problem-solving skills.
Mandatory Domain Experience
- Previous Medtech Supply chain experience is good to have.
- Strong understanding of MedTech Supply Chain business processes and data.
- Ability to work with MTSC stakeholders with minimal domain ramp-up.
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