Data Engineer (m/f/d)

InPost

1d • Hybrid

About The Position

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.

Requirements

• 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 Polish

Nice To Haves

• 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

Responsibilities

• Design, build, and maintain scalable data lake solutions and processing pipelines handling large volumes of structured and semi-structured data.
• Work with both batch and streaming architectures, making deliberate decisions about latency, cost, and reliability trade-offs.
• Build and operate real-time data streaming pipelines using Apache Kafka and its ecosystem (Kafka Streams, Kafka Connect).
• Design event-driven architectures that support use cases ranging from operational monitoring to near-real-time ML feature generation.
• Architect and maintain ETL and ELT pipelines with a focus on data quality, idempotency, and observability.
• Collaborate with data consumers to understand their requirements and translate them into durable, well-tested pipeline designs.
• Develop distributed data processing applications using Apache Spark (PySpark, Scala), running on Databricks.
• Apply Spark best practices — partitioning strategies, broadcast joins, incremental processing — to ensure jobs run efficiently at InPost's scale.
• Design and manage both SQL and NoSQL databases used in our data products.
• Contribute to cloud architecture decisions within your squad.
• 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.
• Treat pipeline code with the same rigour as application code.
• Own the operational health of the data infrastructure and ETL processes you build.
• Set up monitoring, respond to incidents, identify bottlenecks, and implement optimisations to ensure SLAs are met.
• Integrate data from internal and external sources via REST and SOAP APIs, applying patterns for reliable ingestion, schema evolution, and error handling.
• Actively contribute to InPost's data engineering community — through code reviews, internal documentation, tech talks, and mentoring.

Benefits

• 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
• 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
• B2B type of cooperation

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed