Data Steward

• 7+ years of overall experience in Data, Analytics, Data Engineering, Data Governance, or related areas.
• 3+ years of experience in Data Stewardship, Data Quality, Data Analysis, Data Governance, or a closely related technical data role.
• Strong hands-on SQL experience.
• Experience working with large enterprise datasets and multiple source systems.
• Experience with data profiling, reconciliation, data-quality validation, and root-cause analysis.
• Strong understanding of 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.

• Perform hands-on analysis of large and complex MedTech Supply Chain datasets.
• Analyze data across multiple source systems and identify inconsistencies, gaps, anomalies, and data-quality issues.
• Perform source-to-target validation and data reconciliation.
• Define and implement data-quality rules covering:
  • Completeness
  • Accuracy
  • Consistency
  • Uniqueness
  • Timeliness
• Perform data profiling and root-cause analysis for data issues.
• Understand upstream source data, transformation logic, curated data, and downstream data 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.
• Document and validate end-to-end data lineage.
• Identify Critical Data Elements (CDEs) and define appropriate validation rules.
• Analyze production data issues and determine whether the root cause is related to:
  • Source systems
  • Data mappings
  • Transformations
  • Master data
  • Downstream logic
• Work with Engineering teams to 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/governance platforms.
• Work directly with MTSC Business SMEs to understand the business meaning of data and translate business requirements into technical data rules.

• Advanced SQL skills.
• Strong experience with:
  • Complex Joins
  • CTEs
  • Window Functions
  • Aggregations
  • Duplicate Detection
  • Data Validation
  • Data Reconciliation
  • Root-Cause Analysis
• Ability to independently analyze millions of records and identify patterns, anomalies, and data-quality issues.

• Data Profiling
• Data Quality
• Data Validation
• Data Reconciliation
• Source-to-Target Mapping
• Data Lineage
• Metadata Management
• Master Data
• Reference Data
• Data Modeling concepts
• Large-scale Data Analysis

• Python / PySpark
• Databricks
• Azure Data Platform
• Delta Lake / Lakehouse concepts
• SAP Data Analysis
• Power BI or similar analytical tools
• Alation
• Collibra
• Microsoft Purview
• Other enterprise Data Catalog / Data Governance tools
• Git / Version-controlled data artifacts
• Automated Data Quality frameworks

• 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 Ownership
• Business Glossary
• Critical Data Elements (CDEs)
• Metadata
• Data Classification
• Data Lineage
• Data Quality Rules
• Data Standards
• Authoritative Data Sources
• Business Technical Definitions

Required Skills