Data Engineer (Databricks, PySpark, SQL)

Stack

Responsibilities

• ▹Design, develop, optimize and maintain squad-specific data architectures and pipelines in line with ETL and data lake principles
• ▹Prepare, align and hand over data architecture and pipeline artefacts to the platform team for reuse across squads
• ▹Solve complex technical data challenges that support business objectives
• ▹Develop data products for analytics, data scientists and ML engineers
• ▹Advise, mentor and support data and analytics professionals on data standards and best practices
• ▹Contribute to the evaluation of emerging tools in data engineering and data science
• ▹Drive continuous improvement through training, capability development and better analytical data engineering practices and processes

Requirements

• ▹Degree in Computer Science or a related field
• ▹10+ years of experience in software or infrastructure development, including at least 4 years in data engineering within distributed computing, big data or advanced analytics environments
• ▹Strong expertise in SQL and data analysis, with proficiency in at least one programming language such as Python or Scala
• ▹Experience in database development and data modeling, ideally with Databricks/Spark and SQL Server; knowledge of relational, NoSQL and cloud-based databases
• ▹Solid understanding of distributed computing concepts, preferably Spark or MapReduce
• ▹Hands-on experience with Azure tools such as Azure Data Factory, Azure Databricks, Event Hub, Synapse and ML services
• ▹Good understanding of data and analytics concepts: dimensional modeling, ETL, data warehousing, reporting, data governance, structured and unstructured data
• ▹Familiarity with Unix systems, especially shell scripting
• ▹Basic knowledge of network concepts, connectivity and troubleshooting
• ▹Foundational understanding of machine learning, data science, AI, statistics or applied mathematics
• ▹Strong communication skills and fluent English

Nice to have

• ▹German

Soft skills

What we offer

• ▹Development of our own product with high emphasis on quality and long-term stability
• ▹Code quality matters: Clean Code and SOLID principles backed by robust testing
• ▹Flexible working hours and remote work options
• ▹Competitive salary with regular adjustments based on inflation and loyalty
• ▹Continuous learning via an internal learning platform and expert communities