Senior Data Developer (m/f/d)

InPost is a leading European out-of-home (OOH) e-commerce enablement platform with 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, to support rapidly growing parcel volumes.

• Minimum 2 years of experience in Data Engineering, Analytics Engineering, or data analysis, including designing ETL/ELT processes and building data models/layers.
• Experience maintaining production data processes, including monitoring, issue diagnosis, and data quality assurance.
• Experience with cloud solutions, specifically Microsoft Azure.
• Practical knowledge of SQL, Python, PySpark, and Databricks.
• Understanding of Data Lake / Delta Lake architecture and data modeling principles.
• Experience with Git and Azure DevOps for managing changes across 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 prioritization under time pressure.

Nice to have:

• Experience in complex operational environments.
• Knowledge of dimensional modeling including star schema, fact and dimension tables, data grain, 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 such as 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 various source systems.
• Develop data layers in Databricks, Data Lake, and Delta Lake, including tables, views, and data models for analytical and automation solutions.
• Automate and orchestrate data loading and transformation processes to enhance repeatability and reduce manual effort.
• Ensure data quality, consistency, and reliability through validation rules, monitoring, alerting, and incident diagnosis.
• Optimize SQL queries, Spark processes, and data storage for performance, stability, scalability, and cost efficiency.
• Provide reliable data to analysts, Product Owners, and stakeholders for analysis, reporting, and automation.
• Create and maintain technical documentation covering data processes, models, KPI logic, dependencies, data lineage, and incident-handling procedures.
• Use AI tools consciously as accelerators while verifying generated content before implementation.