Data Engineer (mfd)

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 InPosts 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 InPosts data engineering community through code reviews internal documentation tech talks and mentoring. We believe that raising the technical bar is a shared responsibility.

Qualifications :

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

Additional Information :

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

Remote Work :

Yes

Employment Type :

Full-time