In this role, you will
What you will do
Build and maintain data solutions in Databricks using PySpark and SQL. You will work on new requirements as well as read, understand and extend existing notebooks and data processes.
Combine data from different systems and prepare reliable datasets for reports, operational processes and business users.
Create and maintain Delta tables, views and data models, including daily snapshots and reference or mapping datasets.
Translate business requirements into data logic in cooperation with analysts, Product Owners and international teams.
Investigate unexpected results, identify data issues and add practical checks for missing, duplicated or inconsistent data.
Maintain and improve scheduled Databricks jobs that support recurring business processes.
Keep code understandable and maintainable, and improve performance where it affects reliability or processing time.
Curiosity about new technologies and openness to exploring solutions beyond traditional data processing, including automation and containerized applications.
Document data processes, key business rules and common troubleshooting steps.
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
