Data Engineer (Databricks) @ EBIS Remote

Data Engineer (Databricks) @ EBIS

Remote

For our client, we are looking for a specialist with at least 2 years (mid) or at least 5 years (senior) of experience working with Databricks  to join the clients project team, who will:

• work autonomously once business logic and requirements are defined,
• deliver solutions that are testable, maintainable, and resilient,
• actively communicate any issues, uncertainties, or improvement opportunities.

The person in this role will be responsible for developing and maintaining Databricks-based solutions in alignment with business requirements and data engineering best practices.

We are a dynamically developing IT company. We are proud to say that our board is powered by the best Data experts and enthusiasts in the industry, who every day change the face of Data & BI area, thanks to their knowledge and modern technologies.

Our solutions use artificial intelligence, machine learning, cloud computing, Big Data, BI and advanced analytics to provide our customers with the best possible solutions tailored to their needs.

We do not slow down! We are constantly developing, because we are not afraid to take on new challenges.

Together we create the best solutions on the market!

,[Design and implement scalable data pipelines in Azure Databricks using PySpark and SQL., Apply data transformations, including filtering, cleansing, deduplication, and implementation of business logic based on provided specifications., Configure and manage Databricks Workflows, including jobs, clusters, and tasks, while adhering to established naming conventions and scheduling practices., Utilize Git-based development workflows to manage code versioning and deployments., Prepare notebooks for seamless promotion across environments using configuration-driven approaches., Maintain clear and comprehensive documentation, covering notebook objectives, schema lineage, and data loading strategies., Define conventions for primary keys and timestamps., Ensure structured use of Markdown headers, code comments in SQL/Python, and logging practices.] Requirements: Databricks, SQL, PySpark, Python Additionally: Sport subscription, Private healthcare, International projects.

Kategorie

data

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