Senior Data Engineer; m​/f​/d

About the team

As a (Senior) Data Engineer at Adsquare, you will be a key contributor to our core engineering function, creating and maintaining scalable big data pipelines that power our applications and drive business value.

Because our engineering department handles a variety of critical data challenges,
you will be assigned to a specific cross-functional squad based on your individual strengths, experience, and current business needs. To give you an idea of the work, your daily mission might involve:

• Data Ingestion & Products: Developing data solutions built on massive volumes of location signals, geospatial (places) data, and audience attribute data.
• Data Integrations & Egress: Architecting privacy‑first, massive‑scale data egress solutions to ensure our datasets reach external partners reliably, securely, and efficiently.

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.

• 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.

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.

• 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 (No
  
  SQL) databases, and horizontally scalable data warehouses/lake houses (e.g., Redshift, Snowflake, Star Rocks).
• 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.

• Compiled
  
  Languages:
  
  Experience with a compiled or strongly typed language (e.g., Java, Scala, Go, Kotlin, C++, Cython).
• Geospatial Data: Experience working with GIS (Geographic Information Systems) and geo‑spatial datasets.
• Data Formats: Expertise in optimizing file formats (Parquet, Avro, Iceberg) for performance.
• Streaming Technologies: Familiarity with Kafka and Flink.
• Backend Context: Experience working closely with Backend engineers or familiarity with Backend architectural patterns (microservices, API design).

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