• Responsible for the design, development, and maintenance of enterprise data and programming infrastructure supporting HEDIS regulatory reporting and healthcare analytics initiatives. • Design, develop, and maintain scalable ETL/ELT pipelines • Build and optimize data ingestion, transformation, and integration processes using Python and PySpark. • Develop reliable and efficient data workflows. • Create fault-tolerant, maintainable, and high-performing data solutions. • Develop and support cloud-based data solutions utilizing GCP, Databricks, Apache Spark, and related technologies. • Design and develop reports, dashboards, and visualizations using Power BI and Tableau.

• Bewertung und Auswahl von Materialstamm-Daten als Grundlage für eine Agentic-AI-Lösung • Master Data Architecture im Kontext Materialstamm aufsetzen bzw. schärfen • Identifikation und Strukturierung der „schwierigen Fälle" im Material Creation Process • Fachliche Begleitung des Order-Management-/Material-Creation-Prozesses aus MDM-Sicht

• Design, build, and maintain scalable data pipelines for both real-time and historical data processing. • Develop and optimize databases, data warehouses, and ETL workflows handling large-scale time-series data. • Process and manage high-volume data streams (thousands of records per second). • Collaborate with Product, Customer Success, Installation, and Technical Support teams to ensure data quality and business alignment. • Implement monitoring, alerting, and logging systems to ensure platform reliability. • Support Data Science teams by improving data accessibility and facilitating model deployment. • Troubleshoot and optimize data infrastructure performance.

Role 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 - 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. - Streaming Solutions: Build and operate real-time data streaming pipelines using Apache Kafka and its ecosystem (Kafka Streams, Kafka Connect). - ETL/ELT Design and Maintenance: Architect and maintain ETL and ELT pipelines with a focus on data quality, idempotency, and observability. - Spark and Databricks Development: Develop distributed data processing applications using Apache Spark (PySpark, Scala), running on Databricks. - Database Engineering: Design and manage both SQL and NoSQL databases used in our data products. - Cloud-Native Solutions: Build data solutions on cloud infrastructure (GCP, Azure, or AWS). - CI/CD and Engineering Excellence: Apply software engineering best practices to data pipelines. - Performance Monitoring and Optimisation: Own the operational health of the data infrastructure and ETL processes you build. - API and System Integration: Integrate data from internal and external sources via REST and SOAP APIs. - Knowledge Sharing and Community: Actively contribute to InPost's data engineering community. Qualifications - 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 Requirements - 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 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 for employees 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