Databricks Data Engineer/Architect

Tech stack:

• Databricks (Unity Catalog, Delta Live Tables)

• Python (PySpark), SQL

• Azure, AWS, or GCP

• Data Lakehouse, Data Mesh, Data Marts

• DevOps, CI/CD Pipelines

• Agile (Scrum/Kanban)

Requirements:

• At least 8 years in Data Engineering, with a minimum of 2 years specifically in Big Data environments.

• 4+ years of hands-on experience with Databricks services, including data pipelines and Unity Catalog.

• Expert-level skills in Python and SQL.

• Strong background in Data Warehousing, ETL, and distributed data processing.

• Deep understanding of Data Lakes, Data Warehouses, and Data Mesh concepts.

• Experience with at least one public cloud (Azure, AWS, or GCP) and strong design skills for both relational and non-relational storage.

• Analytical mindset capable of troubleshooting complex issues in a big data landscape.

• Very good verbal and written English B2/C1

• Experience working in Agile (Scrum/Kanban) environments.

Project description:

Join our data engineering team as we develop and scale our enterprise data platform. We are building a high-performance ecosystem designed to manage large-scale datasets, ranging from structured to unstructured formats. In this role, you will help modernize our data infrastructure by implementing cutting-edge storage and processing solutions. You will play a key part in designing how we ingest, process, and govern data to provide reliable insights across the organization.

Main responsibilities:

• Design and maintain robust data pipelines and distributed data processing systems using Databricks.

• Implement and manage data governance and security frameworks via Unity Catalog.

• Develop sophisticated data models (Relational and Non-Relational) to support complex analytical requirements.

• Improve the performance and reliability of Big Data workflows and ETL processes.

• Work within an Agile environment, integrating DevOps and CI/CD principles into the data lifecycle.

• Act as a subject matter expert, guiding the team through complex big data challenges and architectural decisions.