Top Data Engineer Jobs in Berlin

Build auditable cloud data pipelines and perform statistical analyses on clinical and medical imaging datasets. Apply classical ML, deep learning, and LLM methods to extract and analyze clinical data, maintain data lineage and reproducibility, create dashboards and observability tooling, and produce evidence suitable for regulatory submissions and audits.

Lead design and implementation of a scalable modern data platform and real-time pipelines. Build, optimize, and maintain data models, ETL/ELT pipelines and integrations (Kafka, Spark, Delta Live Tables), enforce data governance and quality, enable AI/ML workflows and MLOps, mentor the team, evaluate new technologies, and collaborate with product and analytics stakeholders to deliver reliable, self-service data products.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Design and deliver scalable data pipelines for model development and production ML workflows in autonomous driving. Ingest, transform, curate, validate, and monitor large-scale real-world, synthetic, and partner datasets. Improve pipeline performance and collaborate with ML engineers, Data Corpus, AI Platform, and partners to ensure datasets are reliable, traceable, and model-ready.

Design, operate, and optimize scalable ELT pipelines and platform infrastructure for international healthcare data. Build entity-resolution and MDM workflows, implement schema governance and data contracts, optimize cloud storage and warehouse costs, and automate CI/CD for data pipelines while partnering with analytics and domain experts to deliver production-ready datasets.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.

Designs and implements security frameworks for Snowflake and cloud data platforms across GCP and Azure. Automates infrastructure provisioning and security configurations with Terraform, manages GitHub Actions CI/CD security, and optimizes security solutions for threat detection and response. The role requires enterprise data protection, access control, DLP, DevSecOps, SIEM, cloud security, programming, documentation, and cross-functional collaboration with internal teams and vendors.

Design, build, and optimize real-time and batch data pipelines using Apache Beam and GCP services. Integrate multiple data sources, ensure data quality and observability, improve pipeline performance and cost efficiency, and apply CI/CD, infrastructure-as-code, monitoring, governance, security, and compliance practices. Collaborate with data scientists, analysts, and software engineers to deliver reliable data for analytics, AI, and business intelligence.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

As a Senior Data Engineer, you will design scalable data pipelines, implement new technologies, collaborate with Data Scientists, and lead technical aspects while maintaining documentation on data processes.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Build and operate production machine-learning systems for financial-crime transaction monitoring. Responsibilities include developing batch training pipelines, engineering AML and fraud features, deploying and monitoring models, evaluating drift and performance, calibrating thresholds, conducting sensitivity tests, and producing governance documentation. The role partners with AML investigators, fraud operations, product, and engineering teams to deliver explainable, auditable, and scalable ML solutions.

Lead design, implementation, and operation of scalable data pipelines and platform infrastructure on AWS. Define data quality, observability, and operational controls; evolve analytics (ClickHouse, Metabase); build pipelines with Python (pandas, polars), dbt, and dlt; drive cross-team initiatives, mentor engineers, and improve platform reliability and developer experience.

The Senior Data Engineer will design and build data pipelines on GCP, ensuring high data quality and reliability for analytics and ML products. Responsibilities include mastering ETL processes, managing workflows with Airflow, and collaborating closely with ML engineers.

Build and maintain scalable, reliable data infrastructure and self-service features for ETL, orchestration, and real-time analytics. Optimize compute and costs, automate operations for high availability, and contribute to designing and implementing new platform services.

Design and implement security frameworks for data platforms (Snowflake, GCP, Azure); automate infrastructure and security via IaC; manage CI/CD (GitHub Actions/Runners); integrate and operate SIEM and detection tools; collaborate with cross-functional teams and vendors; document processes and ensure compliance.

Design and implement security frameworks for enterprise data platforms (Snowflake, GCP, Azure). Automate infrastructure provisioning and security configs with IaC/Terraform, manage CI/CD via GitHub Actions, integrate security tooling and SIEM for threat detection, and document operational workflows while collaborating with cross-functional teams and vendors.

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.

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

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.

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.