• Design, develop, and operate scalable and maintainable data pipelines in the Azure Databricks environment • Develop all technical artefacts as code, implemented in professional IDEs, with full version control and CI/CD automation • Enable data-driven decision-making in Supply Chain Management (SCM) by ensuring high data availability, quality, and reliability • Implement data products and analytical assets using software engineering principles in close alignment with business domains and functional IT • Apply rigorous software engineering practices such as modular design, test-driven development, and artifact reuse in all implementations • Global delivery footprint; cross-functional data engineering support across SCM domains • Collaboration with business stakeholders, functional IT partners, product owners, architects, ML/AI engineers, and Power BI developers • Agile, product-team structure embedded in an enterprise-scale Azure environment • Design scalable batch and streaming pipelines in Azure Databricks using PySpark and/or Scala • Implement ingestion from structured and semi-structured sources (e.g., SAP, APIs, flat files) • Build bronze/silver/gold data layers following the defined lakehouse layering architecture & governance • Implement use-case driven dimensional models (star/snowflake schema) tailored to SCM needs • Ensure compatibility with reporting tools (e.g., Power BI) via curated data marts and semantic models • Implement enterprise-level data warehouse models (domain-driven 3NF models) for SCM data, closely aligned with data engineers for other business domains • Develop and apply master data management strategies (e.g., Slowly Changing Dimensions) • Develop automated data validation tests using frameworks • Monitor pipeline health, identify anomalies, and implement quality thresholds • Establish data quality transparency by defining and implementing meaningful data quality rules with source system and business stakeholders and implementing related reports • Develop and structure pipelines using modular, reusable code in a professional IDE • Apply test-driven development (TDD) principles with automated unit, integration, and validation tests • Integrate tests into CI/CD pipelines to enable fail-fast deployment strategies • Commit all artifacts to version control with peer review and CI/CD integration • Work closely with Product Owners to refine user stories and define acceptance criteria • Translate business requirements into data contracts and technical specifications • Participate in agile events such as sprint planning, reviews, and retrospectives • Document pipeline logic, data contracts, and technical decisions in markdown or auto-generated docs from code • Align designs with governance and metadata standards (e.g., Unity Catalog) • Track lineage and audit trails through integrated tooling • Profile and tune data transformation performance • Reduce job execution times and optimize cluster resource usage • Refactor legacy pipelines or inefficient transformations to improve scalability

• Design, develop, and maintain data pipelines to ingest, transform, and normalize data from multiple sources. • Lead the design and implementation of data ingestion architectures in Snowflake, ensuring scalability and reliability. • Apply best practices in data validation to ensure accuracy, consistency, and reliability across datasets. • Perform data transformation and normalization to support analytics and reporting use cases. • Work with large datasets using SQL and Python to process and analyze data efficiently. • Ensure data quality by implementing validation rules, checks, and monitoring processes. • Collaborate with business and technical stakeholders to understand data requirements and translate them into scalable solutions. • Support ETL processes and file handling workflows for structured and semi-structured data. • Contribute to documentation and continuous improvement of data processes and standards.

• Assist in designing, planning and managing the data migration process. • Manage assigned risks and monitor potential impacts as part of the data migration plan. • Work with subject matter experts and project team to identify, define, collate, document and communicate the data migration requirements. • Work independently as a self-starter, and within a team environment. • Work across multiple functional projects to understand data usage and implications for data migration. • Develop best practice, processes, and standards for effectively carrying out data migration activities. • Work with customers in gathering technical requirements for data migration needs. • Share knowledge with team members and develop customer solutions efficiently. • Perform source system data analysis in order to manage source to target data mapping. • Perform migration and testing of static data and transaction data from one core system to another.

• This position is responsible for managing and organizing data to support all business processes to achieve corporate and departmental goals • This position is responsible for identifying trends and communicating these trends clearly to others in the organization to ensure data is properly used • Core activities include troubleshooting data issues, and assisting the data architect to develop, align, and maintain architectures with business requirements • Develop data pipelines from various data sources to target locations — including MES, ERP, PLM, SCADA/historian, and quality systems — to support the enterprise Digital Thread, including formatting, cleaning, and updating data as the business needs • Develop and maintain accurate data structure and mapping documentation, including metadata aligned to specific business requirements • Design and publish domain-oriented, reusable data products aligned to a data mesh model with clear ownership, SLAs, and discoverability — standardizing data structure and types across ETL/ELT processes • Understand the big picture, set the right scope, and collaborate with the data architect, application engineers, integration specialists, business analysts, and other technical experts to ensure adequate content delivery to authorized users in a timely, effective, and secure manner • Manage the full life cycle development for the current ETL/ELT deployments, applying software-engineering practices to data pipelines — version control (Git), automated testing, code review, CI/CD, and Infrastructure-as-Code (e.g., Terraform, CloudFormation) — to ensure repeatable, auditable deployments across environments • Partner with manufacturing operations, quality, and supply chain teams to deliver analytics on OEE, yield, scrap, throughput, engineering-change cycle time, time-to-release, and end-to-end product genealogy/traceability supported by the Digital Thread • Prepare AI/ML-ready datasets — including feature pipelines, dataset versioning, and curated knowledge sources for predictive quality, anomaly detection, and retrieval-augmented (RAG) use cases — in partnership with data science and AI teams