Senior Data Engineer - Databricks

Xenit AB•Gothenburg, NE

9d•Hybrid

About The Position

We are seeking a Senior Data Engineer to join a team of experienced experts and driven junior colleagues. We are looking for someone who is passionate about data, analysis, and cloud solutions, with a special focus on Azure and Databricks. You will work with modern data architecture and solutions based on Azure, Databricks, Microsoft Fabric, and Power BI, always with a close connection to business needs and end-users' daily lives. You will be involved in the entire chain - from solution design and data architecture to development, data modeling, and analysis. Together with our clients, you will develop secure, scalable, and future-proof data solutions that create concrete business value. We work with both small and large clients, building long-term relationships where our consultants play an important role in the clients' continued digital and data-driven development.

Requirements

Approximately 5 years of experience in data engineering, data analytics.
Practical experience with Databricks and modern data/lakehouse architectures - preferably Delta Lake and Databricks Lakehouse.
Knowledge of ETL/ELT and data pipelines.
Experience with PySpark and comfort in manipulating large datasets.
Experience with data modeling, data design, and architecture.
Ability to understand both technical and business needs.

Nice To Haves

Power BI.
SQL and optimization of data processes.
CI/CD and version control for data and analytics solutions.

Responsibilities

Design, develop, and maintain modern data flows and data architectures in Azure and Databricks. You will contribute to solution design and architecture for both new and existing data platforms.
Build and further develop Lakehouse solutions and data pipelines with Databricks.
Integrate and process large datasets from various data sources using ETL/ELT, data modeling, and transformation logic.
Develop solutions using technologies like Databricks, Delta Lake, and PySpark pipelines with notebooks or SQL.
Collaborate closely with clients and business stakeholders to transform business needs into scalable data solutions.
Contribute to improving our methods and frameworks within data engineering and data analytics.