• 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

Role Description We are looking for an experienced Senior Data Engineer to join a fast-growing technology company building data-driven solutions at scale. In this role, you will design and maintain robust data infrastructure, process high-volume real-time data streams, and collaborate with cross-functional teams to support analytics and machine learning initiatives. 📍 Location: Philippines (Remote) 💰 Salary: Negotiable 📈 Seniority: Senior Level (5-10 years of experience) Key Responsibilities - 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. Qualifications - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. - 5+ years of experience as a Data Engineer. - Strong experience designing and scaling data infrastructure. - Hands-on experience working with time-series data. - Expert-level SQL skills, especially PostgreSQL and PL/pgSQL. - Ability to analyze and optimize database query execution plans. - Experience with ETL and data pipeline tools such as Airflow, Metaflow, or similar platforms. - Experience with TimescaleDB or other time-series databases. - Strong Python programming skills. - Understanding of data warehousing concepts and best practices. Preferred Qualifications - Excellent problem-solving skills. - Self-driven, proactive, and quick learner. - Experience supporting data science and machine learning workflows. Benefits - Fully remote working environment. - Opportunity to work with cutting-edge data technologies. - Collaborative international team. - Competitive compensation package. - Performance-based incentives. - Comprehensive benefits package. - Strong focus on learning, development, and career growth. - Flexible and autonomous working culture. Recruitment Process - HR Interview - Hiring Manager Interview - Take-home Assignment - Culture Fit Interview