Data Engineer (m/f/d)

InPost has revolutionised e-commerce parcel delivery in Poland and is now one of Europe's leading OOH e-commerce enablement platforms. Founded in 1999 by Rafał Brzoska, InPost provides delivery services through our network of almost 60,000 Automated Parcel Machines (APMs) and almost 35,000 pick-up drop-off points (PUDO) in nine countries across Europe, as well as to-door courier and fulfilment services to e-commerce merchants. InPost's lockers provide consumers with a cheaper and more flexible, convenient, environmentally friendly and contactless delivery option.

Job Description

At InPost, Data & AI is not a support function — it is the engine behind our decisions. We process billions of events daily across nine European markets, and our data platform is what makes that intelligence possible. As a Data Engineer in our Data & AI area, you will be one of the builders: designing the pipelines, streaming systems, and lake architectures that turn raw operational data into reliable, high-quality data products powering ML models, analytics, and business decisions.

You will work in cross-functional squads alongside Data Scientists, Analytics Engineers, and Product Managers, shipping real data products — not internal tooling that no one sees. The scale is real, the data is complex, and the impact is immediate.

Success looks like: data products that are trusted, fresh, and easy to consume; pipelines that run reliably at scale with no manual intervention; and a codebase that your colleagues are proud to contribute to.

Main Activities:

Data Platform & Lake Engineering: Design, build, and maintain scalable data lake solutions and processing pipelines handling large volumes of structured and semi-structured data. You will work with both batch and streaming architectures, making deliberate decisions about latency, cost, and reliability trade-offs.

Streaming Solutions: Build and operate real-time data streaming pipelines using Apache Kafka and its ecosystem (Kafka Streams, Kafka Connect). You will design event-driven architectures that support use cases ranging from operational monitoring to near-real-time ML feature generation.

ETL/ELT Design and Maintenance: Architect and maintain ETL and ELT pipelines with a focus on data quality, idempotency, and observability. You will collaborate with data consumers to understand their requirements and translate them into durable, well-tested pipeline designs.

Spark and Databricks Development: Develop distributed data processing applications using Apache Spark (PySpark, Scala), running on Databricks. You will apply Spark best practices — partitioning strategies, broadcast joins, incremental processing — to ensure jobs run efficiently at InPost's scale.

Database Engineering: Design and manage both SQL and NoSQL databases used in our data products. This includes schema design, query optimisation, and selecting the right storage layer for a given access pattern — from Delta Lake and data warehouses to document stores.

Cloud-Native Solutions: Build data solutions on cloud infrastructure (GCP, Azure, or AWS), leveraging managed services to reduce operational overhead while maintaining performance and cost efficiency. You will contribute to cloud architecture decisions within your squad.

CI/CD and Engineering Excellence: Apply software engineering best practices to data pipelines: version control, automated testing, peer code review, and CI/CD using tools such as GitLab or Jenkins. You will treat pipeline code with the same rigour as application code.

Performance Monitoring and Optimisation: Own the operational health of the data infrastructure and ETL processes you build. You will set up monitoring, respond to incidents, identify bottlenecks, and implement optimisations to ensure SLAs are met.

API and System Integration: Integrate data from internal and external sources via REST and SOAP APIs, applying patterns for reliable ingestion, schema evolution, and error handling.

Knowledge Sharing and Community: Actively contribute to InPost's data engineering community — through code reviews, internal documentation, tech talks, and mentoring. We believe that raising the technical bar is a shared responsibility.

Required:

• At least 3 years of experience in a Data Engineering or similar role
• Hands-on experience with Apache Spark (Streaming, Spark SQL, MLlib) and Databricks (PySpark, Scala)
• Practical experience with Apache Kafka — including Kafka Streams and Kafka Connect
• Proficiency in Python; working knowledge of Scala or Java
• Experience designing and operating SQL databases (e.g., PostgreSQL, BigQuery, Spark SQL) and NoSQL databases (e.g., MongoDB, Cassandra, or similar)
• Experience building and maintaining data lake environments (Delta Lake, Parquet, or equivalent)
• Familiarity with cloud platforms (GCP, Azure, or AWS) and their managed data services
• Experience integrating data via REST and/or SOAP APIs
• Working knowledge of CI/CD tooling (GitLab CI, Jenkins, or equivalent) and software engineering practices (testing, versioning, code review)
• Experience building and running Docker containers
• Willingness to share knowledge and actively contribute to engineering best practices
• Professional working proficiency in both English and Polis

Nice to Have:

• Experience in an international, multi-market environment
• Exposure to ML pipeline engineering or feature store design
• Familiarity with data orchestration tools (Apache Airflow, Prefect, or Databricks Workflows)
• Experience with Infrastructure as Code (Terraform, Ansible)
• Contributions to open-source data engineering projects

​​​​​​Why Join InPost?

• The option to work from the office or 100% remotely
• Opportunity to work in a diverse, international and cross-functional environment, along with leading experts. 
• Fulfilling careers with a range of benefits and invests in providing training opportunities for their development. 
• Involvement in technology monitoring and choices 
• Your impact will be visible instantly and you will be making a difference in our users lives
• We offer B2B type of cooperation