About Onwelo
Onwelo is a technology partner delivering end-to-end services — from strategy and architecture through implementation to maintenance — in the areas of digital transformation, data, and automation. The team has over 400 specialists with strong senior-level competencies and has completed 300+ projects for large organizations in Poland, Europe, and the USA. Onwelo operates from several Polish cities and from offices in Germany, Switzerland, and the USA, combining local presence with global reach.
About the project
We are looking for a Databricks Data Engineer to join one of our ongoing projects for international clients in industries such as finance, retail, and sport & lifestyle. The projects focus on building and modernizing cloud data platforms on Azure, including implementing and developing solutions based on Azure Databricks.
The scope of work covers both migration projects (from on-premise solutions to the cloud) and building data platforms from scratch. Depending on the project, we collaborate with client teams on analyzing existing processes, designing the target architecture, and developing further data processing layers.
Your tasks
•Designing, developing, and maintaining data pipelines in Azure Databricks using Apache Spark (PySpark, SQL)
•Writing efficient, readable, and testable code in Python (OOP), used in data pipelines and supporting data processing components
•Participating in the analysis of existing data processes and designing their target shape in the Azure environment
•Migrating and refactoring data processing processes to a data lake / lakehouse architecture
•Optimizing data processing performance and cloud solution costs
•Collaborating with client and Onwelo teams on data-driven projects
•Ensuring data quality, pipeline stability, and technical documentation
Who we are looking for
•Commercial experience working with Azure Databricks and Apache Spark
•Fluent work with PySpark and SQL
•Advanced knowledge of Python, including designing modular and reusable code (OOP), exception handling, logging, environment configuration, writing tests, and attention to code quality
•Working according to Data Engineering best practices: code versioning, code review, CI/CD, technical documentation
•Knowledge of topics related to data lake, lakehouse, and processing large volumes of data
•Ability to collaborate in a project environment and communicate in English
What we offer
•Flexible hours and the possibility of remote work
•80% of managers and coordinators are promoted internally
•Participation in interesting projects for global clients
•90% of management staff have a technical education
•Access to internal training and development paths
•Participation in team events, company meetings, and Town Halls
•Support in building a professional image (personal branding)
•Access to life insurance and private medical care for yourself and your family
•MultiSport card supporting health and fitness
