Senior Data Engineer – 14305

Veritaz is a leading IT staffing solutions provider in Sweden, committed to advancing individual careers and aiding employers in securing the perfect talent fit. With a proven track record of successful partnerships with top companies, we have rapidly grown our presence in the USA, Europe, and Sweden as a dependable and trusted resource within the IT industry.

Assignment Description:

We are looking for a Senior Data Engineer to join our dynamic team.

What you will work on

• Lead the ongoing migration from the AWS-native data lake to Databricks and support the final stages of delivery.
• Design, build, and optimize high-performance data pipelines and analytics solutions for production environments.
• Develop, maintain, and operationalize next-generation data assets and data models for broad consumption and self-service analytics.
• Collaborate closely with Data Scientists, ML Engineers, Data Analysts, and Data Engineers to enable a data-driven culture and ensure end-to-end delivery from raw data to actionable insights.
• Utilize the Databricks ecosystem, including Lakeflow, Declarative Pipelines, Unity Catalog, MLflow, Model Training, Model Serving, Agent Bricks, AI Gateway, and related tooling.
• Work with Python, PySpark, Bash, and DAX to implement scalable and maintainable solutions.
• Apply strong software engineering practices including GitHub, Git-based workflows, CI/CD pipelines, and high-quality coding standards.
• Provide technical guidance, coach team members, and promote best practices within Machine Learning Engineering and Data Engineering.
• Collaborate within a cross-functional, agile team environment and contribute to continuous improvement of processes, architecture, and solution design.

What you bring

• Senior-level expertise in Data Engineering and Machine Learning Engineering with a strong track record of working in production-grade environments.
• Deep hands-on experience with Databricks, including Lakeflow, Unity Catalog, MLflow, and the broader Databricks engineering and ML tooling landscape.
• Strong proficiency in Python, PySpark, Bash, and DAX.
• Solid understanding of architectural patterns within Data Engineering and Machine Learning Engineering.
• Hands-on experience working with GitHub, CI/CD automation, and modern software engineering practices.
• Good working knowledge of AWS environments and cloud-native data solutions.
• Ability to quickly adapt to established ways of working while contributing modern, state-of-the-art engineering approaches.
• Strong communication and collaboration skills, with the ability to work effectively alongside Data Engineers, ML Engineers, and Data Analysts.
• A proactive, solution-oriented mindset with the ability to coach others and drive technical excellence within the team