Senior Data Developer (m/f/d)

InPost has revolutionised e-commerce parcel delivery in Poland and is now one of Europe’s leading out-of-home (OOH) e-commerce enablement platforms. Founded in 1999 by Rafał Brzoska, InPost provides delivery services through a network of over 64,000 Automated Parcel Machines (APMs) and more than 30,000 pick-up and drop-off (PUDO) points across nine European countries: Poland, the United Kingdom, France, Italy, Spain, Portugal, Belgium, the Netherlands and Luxembourg, alongside to-door courier and fulfilment services for ecommerce merchants.

InPost’s extensive OOH network supports rapidly growing parcel volumes across its markets, with 1.4 billion parcels delivered in 2025. Its locker solutions offer consumers a delivery option that is cheaper, more flexible and convenient, environmentally friendly and contactless. As a leading OOH logistics provider, InPost is recognised for transforming parcel delivery economics in Europe, appealing to both consumers and merchants through its flexible, technology-driven solutions.

Join our Global Network Analytics area

This is a role for someone who enjoys working close to data, understands both technical quality and business context, and wants to have a real impact on how data is prepared, validated and used by analytical, reporting and product teams.

As a (Senior) Data Developer you will help transform data into a trusted foundation for decision-making, reporting and automation. You will combine technical engineering skills with business understanding, data quality awareness and a critical approach to AI-supported work

In this role, you will:

Design, build and maintain ETL/ELT processes using SQL, PySpark and Python, including integration of data from different source systems.

Develop data layers in Databricks, Data Lake and Delta Lake, including tables, views and data models used by analytical, reporting and automation solutions.

Automate and orchestrate data loading and transformation processes to reduce manual work, improve repeatability and lower the risk of errors.

Ensure data quality, consistency and reliability through validation rules, monitoring, alerting and incident diagnosis.

Optimize SQL queries, Spark processes and data storage structures with a focus on performance, stability, scalability and processing costs.

Provide reliable, ready-to-use data to analysts, Product Owners and other stakeholders as a foundation for analysis, reporting and automation

Create and maintain technical documentation in Confluence, covering data processes, models, KPI logic, dependencies, data lineage and incident-handling procedures

Use AI tools consciously as a work accelerator, while fully verifying generated code, configurations and documentation before implementation.

What we are looking for

At least 2 years of experience in Data Engineering, Analytics Engineering or data analysis, including experience in designing ETL/ELT processes and building data models or data layers for analytical purposes.

Experience in maintaining production data processes, including monitoring, issue diagnosis 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.

Practical experience with Git and Azure DevOps, including managing changes across Dev, Test and Prod environments.

The ability to translate business requirements into technical solutions.

Advanced English skills, enabling confident communication in an international environment.

Analytical and logical thinking, attention to detail, proactivity and the ability to prioritize work under time pressure.

Nice to have

Experience working in a complex operational environment.

Knowledge of dimensional modelling, including star schema, fact and dimension tables, data grain, and normalization or denormalization approaches for reporting and analytics.

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 principles.

Experience with Jira and Confluence.

Certifications such as Microsoft Certified: Fabric Analytics Engineer DP-600 or Databricks Data Engineer / Analyst Associate.

Why join InPost?

Real ownership — your data products will directly influence strategic decisions

Opportunity to cooperate in a diverse, international, and cross-functional environment alongside leading experts

Space to experiment with new technologies — including AI tooling — and bring innovations into production

Your impact will be visible immediately

We offer B2B type of cooperation