Data Engineer (m/f/d)

InPost is a leading European e-commerce enablement platform specializing in parcel delivery through a network of Automated Parcel Machines (APMs) and pick-up drop-off points across nine countries. Founded in 1999, it offers delivery and fulfilment services to e-commerce merchants, focusing on flexible, convenient, and environmentally friendly delivery options.

• Minimum 3 years of experience in 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 (PostgreSQL, BigQuery, Spark SQL) and NoSQL databases (MongoDB, Cassandra, or similar).
• Experience building and maintaining data lake environments (Delta Lake, Parquet, or equivalent).
• Familiarity with cloud platforms (GCP, Azure, AWS) and managed data services.
• Experience integrating data via REST and/or SOAP APIs.
• Working knowledge of CI/CD tools (GitLab CI, Jenkins, or equivalent) and software engineering practices.
• Experience building and running Docker containers.
• Willingness to share knowledge and contribute to engineering best practices.
• Professional working proficiency in English and Polish.

Nice to Have:

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

Data Platform & Lake Engineering:

• Design, build, and maintain scalable data lake solutions and processing pipelines for structured and semi-structured data.
• Work with batch and streaming architectures balancing latency, cost, and reliability.

Streaming Solutions:

• Build and operate real-time data streaming pipelines using Apache Kafka and its ecosystem.
• Design event-driven architectures for operational monitoring and near-real-time ML feature generation.

ETL/ELT Design and Maintenance:

• Architect and maintain ETL/ELT pipelines focusing on data quality, idempotency, and observability.
• Collaborate with data consumers to translate requirements into durable pipeline designs.

Spark and Databricks Development:

• Develop distributed data processing applications using Apache Spark (PySpark, Scala) on Databricks.
• Apply Spark best practices to ensure efficient job execution.

Database Engineering:

• Design and manage SQL and NoSQL databases including schema design and query optimization.

Cloud-Native Solutions:

• Build data solutions on cloud platforms (GCP, Azure, AWS) using managed services.
• Contribute to cloud architecture decisions within the squad.

CI/CD and Engineering Excellence:

• Apply software engineering best practices to data pipelines including version control, automated testing, code review, and CI/CD.

Performance Monitoring and Optimisation:

• Own operational health of data infrastructure and ETL processes, set up monitoring, respond to incidents, and optimize performance.

API and System Integration:

• Integrate data from internal and external sources via REST and SOAP APIs with reliable ingestion and error handling.

Knowledge Sharing and Community:

• Contribute to the data engineering community through code reviews, documentation, tech talks, and mentoring.