(Senior) Data Engineer (m/f/d)

Regardless of the specific squad, you will work alongside a talented team of Data and Backend Engineers under the guidance of a Technical Team Lead, operating with a high degree of autonomy and a strong software engineering mindset.

Your Mission

• Data Pipeline Ownership: Take full accountability for the pipeline lifecycle—from raw data ingestion to transformation and external delivery—according to defined SLAs, time, and budget.
• Architect Scalable Solutions: Design and build robust data architectures required to process and transfer terabytes of data.
• Pipeline Optimization: Continuously improve data pipelines for cost and performance. This includes analyzing query plans, optimizing compute and working memory, and strategically applying horizontal or vertical scaling.
• Engineering Rigor: Elevate data engineering standards. Implement CI/CD workflows, infrastructure-as-code, test-driven development (TDD), and automated testing to ensure reliable and maintainable code.
• Data Monitoring: Create and maintain live monitoring dashboards to ensure data solutions are healthy and to support strategic decision-making.
• Collaboration & Mentorship: Bridge the gap between Data and Backend engineering. For Senior applicants, act as a technical leader by mentoring junior team members, conducting code reviews, and introducing best practices.

Your profile

Your Profile

We are looking for a candidate with varying levels of experience (mid-level to senior, typically 3-6+ years) in Data Engineering or Backend Development with a heavy data focus. You must be comfortable working in a self-organized, agile environment.

Must-Have Technical Skills:

• Programming Mastery: Very strong proficiency in Python and SQL . You write modular, production-ready code and possess a solid understanding of both Functional Programming and Object-Oriented Programming (OOP) principles.
• Big Data & PySpark: Deep experience with large-scale data processing frameworks, specifically Apache Spark / PySpark. You understand how to handle TB-scale datasets efficiently. Deep understanding of big data file formats like parquet and avro. Experience with open Lakehouse formats like Iceberg.
• Advanced Optimization Skills: Proven experience in optimizing data pipelines for compute, working memory, and cost efficiency, including reading and analyzing complex query plans/profiles.
• Database & Storage Architecture: Expertise in the trade-offs between OLAP and OLTP systems. You have built solutions using relational and non-relational (NoSQL) databases, and horizontally scalable data warehouses/lakehouses (e.g., Redshift, Snowflake, StarRocks).
• Cloud Native (AWS): Experience architecting solutions within the AWS ecosystem (e.g., S3, Athena, Glue, EMR, Lambda, Batch).
• Infrastructure & Orchestration: Production experience treating infrastructure as software using Terraform , alongside experience with orchestration tools like Airflow, dbt, or Step Functions.
• Engineering Fundamentals: Solid grasp of computer science principles, data structures, algorithms, and git-flow/CI/CD pipelines.
• AI tools: Good command of using AI tools (e.g. Claude Code, Kiro, Gemini Pro) to improve and refactor your code, increase your productivity and quality and performance of your code.

We offer

Nice to Have

Why us?

• Hybrid work model
• 30 vacation days
• Learning budget
• Regular team and company events
• Latest hardware of your choice
• Pet-friendly Berlin office

Recruiting Process

• HybridStep 1: Short 30-min take-home technical quiz.
• Step 2: Value-based interview (30 mins).
• Step 3: Deep-dive technical interview (1.5 hours) with the Data team.
• Step 4: Practical data-crunching challenge.
• Step 5: Team Meet & Greet — the final step to ensure we’re a great fit for each other.

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung