Join our Global Network Analytics area
This is a role for someone who enjoys working close to data, understands both technical quality and business context, and wants to have a real impact on how data is prepared, validated and used by analytical, reporting and product teams.
As a (Senior) Data Developer you will help transform data into a trusted foundation for decision-making, reporting and automation. You will combine technical engineering skills with business understanding, data quality awareness and a critical approach to AI-supported work
In this role, you will:
- Design, build and maintain ETL/ELT processes using SQL, PySpark and Python, including integration of data from different source systems.
- Develop data layers in Databricks, Data Lake and Delta Lake, including tables, views and data models used by analytical, reporting and automation solutions.
- Automate and orchestrate data loading and transformation processes to reduce manual work, improve repeatability and lower the risk of errors.
- Ensure data quality, consistency and reliability through validation rules, monitoring, alerting and incident diagnosis.
- Optimize SQL queries, Spark processes and data storage structures with a focus on performance, stability, scalability and processing costs.
- Provide reliable, ready-to-use data to analysts, Product Owners and other stakeholders as a foundation for analysis, reporting and automation
- Create and maintain technical documentation in Confluence, covering data processes, models, KPI logic, dependencies, data lineage and incident-handling procedures
- Use AI tools consciously as a work accelerator, while fully verifying generated code, configurations and documentation before implementation.
What we are looking for
- At least 2 years of experience in Data Engineering, Analytics Engineering or data analysis, including experience in designing ETL/ELT processes and building data models or data layers for analytical purposes.
- Experience in maintaining production data processes, including monitoring, issue diagnosis and data quality assurance.
- Experience with cloud solutions, especially Microsoft Azure.
- Practical knowledge of SQL, Python, PySpark and Databricks.
- Understanding of Data Lake / Delta Lake architecture and data modelling principles.
- Practical experience with Git and Azure DevOps, including managing changes across Dev, Test and Prod environments.
- The ability to translate business requirements into technical solutions.
- Advanced English skills, enabling confident communication in an international environment.
- Analytical and logical thinking, attention to detail, proactivity and the ability to prioritize work under time pressure.
Nice to have
- Experience working in a complex operational environment.
- Knowledge of dimensional modelling, including star schema, fact and dimension tables, data grain, and normalization or denormalization approaches for reporting and analytics.
- Knowledge of advanced Databricks and Delta Lake mechanisms.
- Experience with streaming technologies such as Kafka, Structured Streaming or Event Hubs.
- Familiarity with monitoring and alerting tools.
- Knowledge of data security, access control, metadata management and data lineage principles.
- Experience with Jira and Confluence.
- Certifications such as Microsoft Certified: Fabric Analytics Engineer DP-600 or Databricks Data Engineer / Analyst Associate.
Why join InPost?
- Real ownership — your data products will directly influence strategic decisions
- Opportunity to cooperate in a diverse, international, and cross-functional environment alongside leading experts
- Space to experiment with new technologies — including AI tooling — and bring innovations into production
- Your impact will be visible immediately
- We offer B2B type of cooperation
