Data Engineer (f/m/x)

Mercedes-Benz.io GmbH•Waller, WA

23h•Hybrid

About The Position

Mercedes-Benz.io is seeking a Data Engineer to join the Data Operations Hub (DOH), which drives the transformation towards a unified and efficient data ecosystem for Mercedes-Benz Digital Marketing and Sales. As a Data Engineer within DOH, you will play a key role in building and maintaining the data foundations that power analytics, reporting, and data-driven decision-making across Mercedes-Benz. The role involves designing and building scalable data pipelines, creating data processing infrastructure, leveraging Azure cloud services, developing dimensional data models, writing efficient Python and SQL code, and working extensively with Apache Spark. You will collaborate with stakeholders, ensure data governance standards, support ML pipelines, and continuously improve platform performance.

Requirements

3 to 5 years of hands-on software engineering experience building, maintaining, and optimizing data pipelines.
Strong experience applying software engineering best practices, including object-oriented programming (OOP), test-driven development (TDD), CI/CD and version control.
Proficiency in PySpark and SQL including query optimization and a solid understanding of Spark internals.
Strong Python programming skills for data processing, automation, and scalable data engineering solutions.
Proven expertise with Azure cloud technologies, including Databricks, Data Factory, Azure DevOps, and Data Lake Storage.
Solid experience in dimensional data modelling and data warehousing principles.
Strong analytical and problem-solving skills, with the ability to troubleshoot complex data challenges.
Excellent communication skills and the ability to work effectively with technical and non-technical stakeholders.
A collaborative mindset and the ability to thrive in a fast-paced, team-oriented environment.

Nice To Haves

Experience with Kubernetes and Docker for containerized applications.
Experience working with Google Analytics data.
Knowledge of machine learning deployment and monitoring practices.
Experience working in large-scale cloud-based data platforms and modern analytics environments.

Responsibilities

Design and build scalable data pipelines for cleaning, integrating, and transforming large datasets.
Create data processing infrastructure that ensures high-quality data availability for digital analysts and data scientists.
Leverage Azure cloud services including Data Factory, Databricks, and Data Lake Storage to develop robust and scalable data solutions.
Develop dimensional data models and maintain data warehousing solutions that support analytics and reporting needs.
Write efficient and well-tested Python and SQL code for large-scale data processing.
Work extensively with Apache Spark developing and optimizing PySpark code and SQL queries while applying a deep understanding of Spark internals.
Collaborate closely with data analysts and stakeholders to understand business requirements and support data-driven decision-making.
Ensure data governance standards are met by monitoring data quality and implementing best practices for data management.
Support the deployment, monitoring, and maintenance of machine learning pipelines and data solutions in production environments.
Continuously improve platform performance, scalability, and reliability through automation and engineering best practices.