Senior Data Engineer - 14305

Om jobbet

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