IT Engineer - Data Lakehouse

• 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

• 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

• 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

• 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

• 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 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

• 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

• 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

• 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

• 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

• Strong experience with modern data engineering technologies, including Azure Databricks, PySpark, Scala, Delta Lake, and Azure Data Lake Storage (ADLS Gen2) .

• Strong experience with modern data engineering technologies, including Azure Databricks, PySpark, Scala, Delta Lake, and Azure Data Lake Storage (ADLS Gen2) .
• Solid understanding of data modeling, ETL/ELT architecture, orchestration frameworks, and scalable data platform design .
• Experience with CI/CD pipelines, DevOps practices, Git-based development workflows, and Infrastructure as Code .
• Proficiency in agile software delivery methods , including Scrum, SAFe, test-driven development, and collaborative development practices.
• Good knowledge of data warehouse concepts , including dimensional modeling, normalization, star schemas, and Slowly Changing Dimensions (SCDs).
• Understanding of data governance, metadata management, and best practices for operating lakehouse environments .
• Familiarity with business intelligence and reporting solutions , preferably including Power BI and tabular analysis tools.
• Basic understanding of advanced analytics, data mining, machine learning concepts, and AI-driven use cases .
• Knowledge of AI services, large language models (LLMs), and API integration concepts .
• Understanding of supply chain management processes, data structures, and business KPIs is beneficial.