Data Expert (m/f/d)

InPost is a leading out-of-home (OOH) e-commerce enablement platform in Europe, founded in 1999 and operating a network of over 64,000 Automated Parcel Machines and more than 30,000 pick-up and drop-off points across nine European countries. The company specializes in delivery services, including locker solutions and courier services for e-commerce merchants, handling 1.4 billion parcels in 2025.

• Minimum 2 years of experience in Data Engineering, Analytics Engineering, or data analysis with expertise in ETL/ELT process design and building analytical data models.
• Experience maintaining production data processes including monitoring and quality assurance.
• Experience with cloud platforms, especially Microsoft Azure.
• Practical knowledge of SQL, Python, PySpark, and Databricks.
• Understanding of Data Lake and Delta Lake architecture and data modeling principles.
• Experience with Git and Azure DevOps, managing changes across multiple environments.
• Ability to translate business requirements into technical solutions.
• Advanced English communication skills.
• Analytical thinking, attention to detail, proactivity, and ability to prioritize under pressure.

Nice to have:

• Experience in complex operational environments.
• Knowledge of dimensional modeling (star schema, fact/dimension tables, data grain, normalization/denormalization).
• Knowledge of advanced Databricks and Delta Lake features.
• 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, including data integration 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 errors.
• 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.
• Provide reliable, usable data for analysts, Product Owners, and other stakeholders.
• Create and maintain technical documentation in Confluence covering data processes, models, KPI logic, dependencies, data lineage, and incident handling.
• Use AI tools as accelerators while verifying generated code and documentation before implementation.