Top Data Engineer Jobs in Berlin

Build and operate production integrations between Luna and third-party practice management systems. Design scalable infrastructure, ensure data consistency and reliability through idempotency, retries, error handling, and observability, and triage production failures. Establish technical standards, architecture, and processes for scalable integration development and incident handling. The role requires strong programming skills, distributed-systems knowledge, production experience, and clear communication in a fast-paced environment.

Designs, builds, and improves data workflows, backend services, AI agents, pipelines, and workflow orchestration systems. The role centralizes fragmented tools, supports production-grade internal platforms, improves reliability and automation, and translates analyst and stakeholder requirements into scalable technical solutions. Requires strong Python, SQL, data modeling, AI application, Git, and data engineering skills, with collaboration across analysts, engineers, and business stakeholders.

Design, build, and optimize large-scale batch and streaming ETL pipelines and ML infrastructure for real-time recommender systems. Implement high-throughput/low-latency data stores, ensure data governance and quality, collaborate with product, data science, and engineering teams, and influence architecture and team objectives to deliver measurable business impact.

Architect, implement, scale, and maintain petabyte-scale data processing platforms for frontier AI models. Build reliable, observable, cost-efficient pipelines with heterogeneous and GPU-accelerated workloads, including failure recovery and restartability. Ensure datasets and intermediate outputs are versioned, reproducible, traceable, and documented with datasheets. Partner with AI researchers to integrate datasets into training pipelines and guide sustainable technology decisions.

Design and operate production data pipelines and standardized data models for a multi-tenant Azure SaaS platform serving container terminals. Build near-real-time and batch ELT pipelines from operational systems, develop Go-based connectors, implement data quality monitoring, and support source-to-curated transformations across PostgreSQL layers. Collaborate directly with terminal operations staff while owning architectural decisions and platform scalability.

Build and operate reliable fintech data products supporting fraud prevention, including batch pipelines, historization, data quality controls, observability, and point-in-time-correct datasets. Partner with data science and fraud operations teams to enable model training, backtesting, inference, and real-time rule evaluation. Own solutions across design, development, deployment, monitoring, and continuous improvement while integrating batch systems with Apache Flink streaming infrastructure.

Develop and scale web-scale data pipelines that transform raw text into high-quality machine learning datasets. Responsibilities include deduplication, model-based scoring, filtering, toxicity and PII removal, metadata extraction, dataset versioning, provenance tracking, compliance, leakage detection, quality monitoring, and researcher tooling. The role also involves acquiring and evaluating text corpora, collaborating with research, engineering, legal, and governance teams, and developing novel data transformations for AI training.

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.

Lead the design, implementation, and operation of enterprise data platforms, scalable ELT/ETL pipelines, master data structures, lakehouse and semantic models, and Power BI reporting. Own a cost-management data solution, establish data quality and governance controls, and implement DevOps practices. Partner with finance, operations, and business stakeholders to deliver secure, trusted, high-performing data products and provide technical leadership across architecture, deployment, and support.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Build and own scalable Azure data platforms, SQL Server and Databricks environments, end-to-end pipelines, and maintainable data models. Apply Kimball dimensional modeling, solve complex client data problems, challenge unclear requirements, and communicate technical concepts clearly to technical and non-technical stakeholders. The role emphasizes production reliability, client impact, continuous learning, and collaboration in a fully remote European data consultancy.

Own and scale the data platform supporting multi-tenant industrial optimization applications. Responsibilities include ingesting heterogeneous customer data, designing storage and query paths for millions of records, managing PostgreSQL performance and migrations, building infrastructure and deployment systems, improving optimization-engine performance, and shipping and operating features in production. The role works across Python, TypeScript, cloud infrastructure, APIs, data pipelines, and the broader product stack.

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.

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.

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.

Lead research, design, and production implementation of algorithms and predictive models for warehouse scheduling, planning, and routing. Deliver end-to-end solutions from prototype to highly available production services, mentor applied scientists, and influence scalable, data-driven architecture and development methodologies.

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 the architecture and development of scalable data models using Data Vault 2.0, dbt, BigQuery, and Airflow. Establish modeling standards, data quality, lineage, governance, testing, and CI/CD practices. Optimize cloud data warehouse performance and costs, oversee orchestration workflows, collaborate with analytics and business stakeholders, mentor engineers, and address scalability challenges across modern cloud data platforms.

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.