Build and run our central Data Foundation platform, the single source of truth for the organization
Work mainly in Azure with a lakehouse architecture (Azure Data Lake, Data Factory, Databricks) to collect, transform and consolidate data from ERPs, CRMs and other systems
Enable analytics and Power BI reporting for Finance, Sales, Logistics, Sustainability, Management and other functions
Design, develop and maintain data pipelines and models in our Data Foundation platform
Build and optimize ingestion in Azure Data Factory from ERPs, CRMs and internal/external sources
Develop and maintain transformation logic in Databricks (SQL and Python) and implement scalable lakehouse / dimensional models
Profile, map and validate data to ensure high data quality and consistency for downstream analytics
Monitor and operate pipelines and platform components (scheduling, performance, availability, error handling) according to SLAs
Troubleshoot and resolve incidents, perform root cause analysis and implement preventative improvements
Act as a 2nd/3rd line expert for data platform and pipeline issues
Contribute to architecture, standards, reusable components and best practices
Collaborate with business stakeholders, application owners, BI developers, data analysts, architects and vendors to translate requirements into data solutions
Create and maintain technical documentation for pipelines, datasets and models

Bachelor’s degree in computer science, Information Systems, Engineering, Mathematics or similar, or equivalent experience
3–5+ years as Data Engineer, BI/Data Warehouse Developer or similar
Hands-on experience with Azure Data Lake, Azure Data Factory and Databricks
Strong skills in building and operating batch and/or streaming data pipelines in a lakehouse or data warehouse
Solid SQL and Python skills for data engineering, ideally in Databricks
Good understanding of relational/analytical databases and performance optimization
Knowledge of data governance, data quality, security and handling of sensitive data
Experience with monitoring, logging, alerting and ITSM processes (incident/problem/change management)
Familiarity with CI/CD, version control and automated testing for data pipelines

Data Engineer

Key skills