IT engineer data lakehouse

Continental

5d • Hybrid

About The Position

The IT Digital and Data Services Competence Center of ContiTech caters to all the Business Areas in ContiTech and is responsible for areas of Data & Analytics, Web and Mobile Software Development, and AI. The team for Data services specializes in all platforms, business applications, and products in the domain of data and analytics, covering the entire spectrum including AI, machine learning, data science, data analysis, reporting, and dashboarding. This role focuses on designing, developing, and operating scalable and maintainable data pipelines in the Azure Databricks environment, enabling data-driven decision-making in Supply Chain Management (SCM) by ensuring high data availability, quality, and reliability. The position involves implementing data products and analytical assets using software engineering principles in close alignment with business domains and functional IT, applying rigorous software engineering practices, and supporting a global delivery footprint with 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 is key, within an agile, product-team structure embedded in an enterprise-scale Azure environment.

Requirements

• Degree in Computer Science, Data Engineering, Information Systems, or related discipline.
• Certifications in software development and data engineering (e.g., Databricks DE Associate, Azure Data Engineer, or relevant DevOps certifications).
• 3–6 years of hands-on experience in data engineering roles in enterprise environments.
• Demonstrated experience building production-grade codebases in IDEs, with test coverage and version control.
• Proven experience in implementing complex data pipelines and contributing to full lifecycle data projects (development to deployment)
• Experience in at least one business domain: SCM or a comparable field

Nice To Haves

• Experience mentoring junior developers or leading implementation workstreams is a plus
• Experience working in international teams across multiple time zones and cultures, preferably with teams in India, Germany, and the Philippines.

Responsibilities

• 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

Benefits

• Training opportunities
• Mobile and flexible working models
• Sabbaticals

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level