Data Engineer (Databricks)

Join the Central Europe Backend Engineering team as a Data Engineer with Databricks and help shape the data landscape. Design, develop, and implement robust data pipelines for shipment, sell-out, and market-share data across Central Europe.

This is a high-impact opportunity supporting 12+ digital products and 700 users across the region. The work enables analytics, data-driven decision-making, and continuous improvement of business strategies.

Key Responsibilities

• Design and develop high-quality Databricks code using PySpark notebooks and SQL to meet business requirements; this comprises at least 70% of the role.
• Use AI capabilities, such as GitHub Copilot or industry tools such as BMAD, to improve productivity and accelerate development.
• Assemble and prepare large, complex datasets that meet functional and non-functional requirements for diverse applications.
• Partner with data asset managers, architects, and development leads to ensure technical data solutions align with architectural blueprints and deliver reliable data.
• Contribute to and use coding standards and best practices to build efficient, scalable, and reusable services and components.
• Collaborate with front-end teams with a data-as-a-product mindset to enable seamless data delivery and integration.
• Apply sound development practices and agreed architectural designs throughout the development lifecycle.
• Identify and define infrastructure revamp initiatives that reduce technical debt and improve system longevity.
• Deliver data-engineering projects as a member of a Scrum team, aligned with business priorities and agile methodologies.
• Provide timely L3 support for existing data processes; analyze bugs and incidents to maintain stability and performance.
• Identify, design, and implement internal process improvements to streamline and automate backend operations.
• Stay current with data-engineering and data-management trends, technologies, and best practices; share knowledge and drive innovation.

Qualifications

• Strong proficiency in PySpark for data processing, transformation, and analysis.
• Proven hands-on experience with Databricks, including cluster management, notebook development, and job scheduling.
• Advanced SQL skills for complex data manipulation, querying, and performance tuning.
• Experience designing, implementing, and optimizing robust data pipelines and ETL/ELT processes using PySpark and Databricks.
• Familiarity with data modeling, data warehousing concepts, and dimensional modeling techniques.
• Understanding of data integration patterns, data lake architectures, and data-quality best practices.
• Experience with Databricks Workflow management and data-pipeline orchestration is an advantage.
• Experience with Google Cloud Platform (GCP) is an advantage.

Benefits

• Access to large-scale projects and leading IT partners and technologies from day one.
• Training and certification paths for self-development.
• Competitive starting salary and benefits, including private health care, stock, saving plans, and sport cards.
• Regular salary increases and promotion opportunities based on results and performance.
• Opportunity to change roles every few years.
• Hybrid work model: work from home up to two days per week and collaborate in the office.

Employment Conditions

• Employment is offered exclusively under an Umowa o Pracę (full-time employment contract). Apply only if you agree to these conditions.

Equal Opportunity

Equal opportunity employer that values diversity and does not discriminate on legally protected grounds.