(Senior) Data Developer (m/f/d)

InPost is a leading European out-of-home (OOH) e-commerce enablement platform operating across nine European countries, providing delivery services through a network of Automated Parcel Machines (APMs) and pick-up and drop-off points, supporting over 1.4 billion parcels delivered in 2025.

• At least 2 years of experience in Data Engineering, Analytics Engineering, or data analysis, including design of ETL/ELT processes and building data models.
• Experience maintaining production data processes, including monitoring and data quality assurance.
• Experience with cloud solutions, especially 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 across Dev, Test, and Prod environments.
• Ability to translate business requirements into technical solutions.
• Advanced English skills for confident communication in an international environment.
• 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, star schema, fact/dimension tables, data grain, normalization/denormalization.
• Advanced Databricks and Delta Lake mechanisms knowledge.
• 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 different sources.
• Develop data layers in Databricks, Data Lake, and Delta Lake including tables, views, and data models.
• Automate and orchestrate data loading and transformation to reduce manual work and errors.
• Ensure data quality, consistency, and reliability via validation rules, monitoring, and incident diagnosis.
• Optimize SQL queries, Spark processes, and data storage for performance and scalability.
• Provide ready-to-use data to analysts, Product Owners, and stakeholders.
• Create and maintain technical documentation in Confluence covering data processes, models, KPIs, dependencies, data lineage, and incident handling.
• Use AI tools as accelerators while verifying generated code and documentation before implementation.