Senior Data Developer (m/f/d)

InPost is a leading European out-of-home (OOH) e-commerce enablement platform with a network of over 64,000 Automated Parcel Machines (APMs) and more than 30,000 pick-up and drop-off points across nine European countries. The company specializes in parcel delivery services including locker solutions and door-to-door courier and fulfillment services, delivering 1.4 billion parcels in 2025.

• Minimum 2 years of experience in Data Engineering, Analytics Engineering, or data analysis including designing ETL/ELT processes and building data models
• Experience maintaining production data processes, monitoring, issue diagnosis, and data quality assurance
• Experience with cloud solutions, notably Microsoft Azure
• Practical knowledge of SQL, Python, PySpark, and Databricks
• Understanding of Data Lake / Delta Lake architecture and data modelling principles
• Experience with Git and Azure DevOps managing changes in Dev, Test, and Prod environments
• Ability to translate business requirements into technical solutions
• Advanced English communication skills
• Analytical and logical thinking, attention to detail, proactivity, and ability to prioritize under pressure

Nice to have:

• Experience in complex operational environments
• Knowledge of dimensional modelling including star schema and normalization/denormalization
• Knowledge of advanced Databricks and Delta Lake mechanisms
• Experience with streaming technologies such as Kafka, Structured Streaming or Event Hubs
• Familiarity with monitoring and alerting tools
• Knowledge of data security, access control, metadata management, and data lineage
• Experience with Jira and Confluence
• Certifications like Microsoft Certified: Fabric Analytics Engineer DP-600 or Databricks Data Engineer / Analyst Associate

• Design, build and maintain ETL/ELT processes using SQL, PySpark and Python, integrating data from multiple sources
• Develop data layers in Databricks, Data Lake, and Delta Lake including tables, views, and data models
• Automate and orchestrate data loading and transformation processes to improve repeatability and reduce manual work
• Ensure data quality, consistency, and reliability through validation, monitoring, alerting and incident diagnosis
• Optimize SQL queries, Spark processes, and data storage for performance, scalability, stability, and cost
• Provide reliable data to analysts, Product Owners, and stakeholders for analysis, reporting, and automation
• Create and maintain technical documentation in Confluence covering data processes, models, KPIs, dependencies, data lineage, and incident handling
• Use AI tools consciously as a work accelerator while verifying generated outputs before implementation