Top Data Engineer Jobs in Berlin

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.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

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.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

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.

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.

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.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

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

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

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.

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.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

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.

Design, build, and operate scalable data infrastructure on AWS and Kubernetes. Develop streaming and platform components (Kafka, Snowflake), implement IaC (Terraform) and CI/CD (GitLab), build monitoring/observability, own features end-to-end, collaborate with analysts and data scientists, and participate in on-call rotations to ensure platform reliability and performance.

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.

Design, build, and operate cloud-native, serverless ClickHouse infrastructure. Develop and maintain Kubernetes operators, improve metrics pipelines, support data ingestion/transformation systems (Spark/Kafka), and collaborate with core database and data plane teams to ensure scalable, highly available distributed systems.

Build and maintain scalable backend services, data ingestion pipelines, and data platforms. Improve reliability, observability and cost-efficiency of data infrastructure, develop reusable tooling, automate orchestration, and collaborate with engineering and data teams to deliver high-performance data solutions.

Lead design and implementation of customer-facing data intelligence applications: build shared API layers, low-latency data-access and caching strategies, advanced interactive visualizations, LLM-enabled interfaces, and mentor engineers while shaping architecture and standards.

Lead and operate production-grade data pipelines and streaming systems to ingest, enrich, process, and serve web event and intent data at scale. Own cloud infrastructure as code, observability, data quality, and CI/CD. Collaborate with product and ML teams to deliver reliable, low-latency data powering customer-facing features and AI/ML workflows.