Top Data Engineer Jobs in Berlin

Develop and maintain scalable data infrastructure and ETL pipelines, support machine learning applications, ensure data quality, and collaborate on data models.

As a Data Analyst at Air Apps, you'll analyze user behavior and product performance, create reports and dashboards, and provide actionable insights using data visualization tools and programming languages.

The role involves building and maintaining data pipelines using various technologies, collaborating with stakeholders, monitoring data health, and promoting team knowledge sharing.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

As a Data Engineer, you will manage laboratory and clinical data projects, building machine learning models and supporting data analytics in the life sciences domain.

Design, implement, and maintain Python backend services and CI/CD/GitOps pipelines on AWS/Kubernetes. Build and maintain data models (Avro, SQL), publish via Kafka and schema registry, create access-layer models in Snowflake, and coordinate between Data and Tech teams to align schemas, priorities, and timelines.

Design, implement and operate data systems that power marketing campaign decisions. Build and tune distributed data pipelines, optimize storage performance, own end-to-end projects, mentor teammates, and collaborate with product and business stakeholders to improve platform quality and observability.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

Operate and improve the database platform by building scalable, secure cloud infrastructure, automating provisioning, enhancing observability, participating in on-call rotations, and collaborating with product teams to meet SLOs.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Leading and mentoring a data platform engineering team while overseeing platform architecture, development, and compliance with security and governance in a cloud environment.

Design, implement, and monitor data pipelines for Better Planner to supply ML models with timely, clean data. Support developers and analysts across countries, improve the shared data platform, identify data quality issues, and provide technical mentorship and best-practice guidance to the tribe.

The Software Engineer will support data collection for AI model training by enhancing the ingestion pipeline, collaborating with scientists, and managing cloud infrastructure.

Own, design, and develop the AI Data Platform: maintain a high-scale data product platform (≈400K pipeline jobs/month, petabyte scale), architect cross-cloud data infrastructure, build and operate automated data pipelines, ensure data observability and quality, and implement AI-driven features and AI data agents to expose AI-ready data products.

Develop backend services using Rust for cloud-native systems processing terabytes of data. Contribute to innovation in observability solutions.

Responsible for deploying and maintaining data center infrastructure, troubleshooting hardware and network issues, managing inventory, and setting up data centers.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

The Senior Data Platform Engineer will enhance the data platform infrastructure, collaborate with teams, implement infrastructure-as-code, optimize workloads, build CI/CD pipelines, and ensure observability and monitoring.

Lead migration of legacy Hadoop/Spark/Impala data pipelines to a cloud-native Databricks platform. Design and build scalable Airflow/Databricks ETL pipelines, ensure data quality, improve observability and reliability, and use AI coding assistants to accelerate modernization while validating performance and architecture.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Design, build, and maintain scalable batch and real-time data pipelines; evolve the warehouse/lakehouse architecture for performance and reliability; implement data quality, monitoring, and observability; collaborate with Product, Analytics, Operations, and Engineering; take end-to-end ownership of data initiatives and optimize processing efficiency, query performance, and cost.

Lead technical vision and architecture for enterprise data platforms. Own end-to-end platform design from ingestion to distribution, set engineering standards, mentor senior engineers, and collaborate with security, compliance, product, and stakeholders to deliver scalable, reliable, and compliant data platform capabilities.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Lead migration from Snowflake to the client platform, build AI-native data migration capabilities, design and maintain scalable data pipelines and platforms, propose improvements, and collaborate with senior engineers, product/project managers, and data analysts.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.