Top Data Engineer Jobs in Berlin

Lead data architecture, modeling, and engineering for high-volume event and analytics datasets. Build reliable batch and streaming pipelines, standardize data products, and partner with Applied Scientists to productionize statistical and machine learning models. Establish data quality, testing, monitoring, and performance standards while optimizing reliability and cost. Mentor engineers and drive large-scale architecture initiatives across multidisciplinary teams.

Build and maintain scalable production ELT pipelines using Python, SQL, and AWS. Responsibilities include evolving data platform architecture, ensuring data quality, optimizing transformations, monitoring workflows, troubleshooting issues, documenting models, and collaborating with engineering and product teams. The role supports analytics, reporting, and product features while using modern data architecture and cloud technologies.

Design, build, and maintain data pipelines supporting AI and machine learning systems. Integrate, version, monitor, and validate training and inference data; develop automation tooling; and manage data quality across large-scale hybrid infrastructure. Collaborate with AI Engineers, Perception, and Data Infrastructure teams while supporting production systems, on-call duties, data annotation, and external service providers.

Build and maintain batch and streaming data pipelines and data products using Python, Airflow, dbt, Pub/Sub, and BigQuery. Develop data quality, observability, monitoring, and alerting frameworks; manage Terraform-based GCP infrastructure, access controls, cost optimization, and GDPR compliance. Collaborate with engineers, data scientists, analysts, product managers, and operations teams while developing AI tooling to improve data engineering productivity and self-service platform usage.

Build and operate production data pipelines for patent, research, and company data using Python and Dagster. Model messy real-world data, develop agent evaluation harnesses, implement data quality monitoring, and deliver reliable data to product stores and APIs. Collaborate with product and ML teams, evaluate new data sources, troubleshoot source failures, and define measurable standards for data quality. The role involves working with large, noisy datasets and shaping how AI agents support data processing.

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Leads the strategy, architecture, and operation of scalable data platforms, storage, and pipelines. Builds and manages the data infrastructure team, ensures platform reliability and cost efficiency, and partners with engineering, AI, perception, and simulation teams. Establishes data security, access control, governance, versioning, monitoring, and compliance standards for very large data volumes.

Own and evolve the platform that turns research models and data into reproducible, auditable client deliverables. Lead Python-based data infrastructure, CI/CD, pipeline orchestration, GCP architecture, infrastructure-as-code, monitoring, access management, and geospatial workflows. Review designs, establish engineering standards, mentor engineers, and coordinate infrastructure priorities. The role also involves production data systems, schema design, model versioning, containers, observability, and collaboration with software, data, science, and solutions teams.

Own process safety, control philosophy, instrumentation, P&IDs, interlocks, and automation architecture for a chemical pilot and demo plant. Lead HAZOP, LOPA, SIL, ATEX, and regulatory activities; manage skid suppliers through design, FAT, and SAT; and oversee control cabinets and campaign operations. Build data acquisition, historian, batch-record, and modeling infrastructure using Python and process simulation tools to ensure safe, controlled, and reproducible chemical processing.

Design, build, and operate Python and TypeScript microservices, data pipelines, integrations, and LLM-powered workflows. Own PostgreSQL data modeling, asynchronous processing, observability, performance, deployments, and production incidents end to end. Drive architecture decisions, technical designs, testing, and monitoring while mentoring engineers. The role also contributes to web crawling, CRM and email integrations, automation, and AI-enhanced development workflows.

Build and evolve Trade Republic’s analytics data platform, including query experiences, conversational analytics, dashboards, integrations, and Airflow orchestration. Engineer resilient, observable systems at scale by defining SLOs, error budgets, testing, safe rollouts, incident reviews, and automated guardrails. Own the platform roadmap, partner with product managers, evaluate technology trade-offs, and create opinionated paved roads that simplify data use for hundreds of consumers.

Own end-to-end industrial AI deployments, from defining customer use cases and onboarding machine data to validating analytics, training operators, and driving adoption. Diagnose production and data issues using Python, SQL, statistics, time-series analysis, KPIs, anomaly detection, and root-cause analysis. Collaborate with operators, plant managers, IT, OT, and commercial teams, manage customer projects, identify expansion opportunities, and feed field insights back into the product. The role is hands-on, customer-facing, Germany-based, and involves approximately 20% travel to customer sites.

Own and evolve Grover’s data platform, including high-volume ingestion pipelines, AWS and warehouse automations, Apache Airflow orchestration, observability, data quality, migrations, and infrastructure reliability. Optimize Snowflake performance and costs through usage tracking and FinOps practices. Partner with Analytics Engineers and data champions to establish scalable architecture, technical standards, documentation, and decentralized analytics enablement.

Design and implement a greenfield Databricks Lakehouse for CRM data, integrating Salesforce, Redshift, and other sources. Build scalable ETL/ELT pipelines, Medallion architecture, ML infrastructure, reverse ETL, monitoring dashboards, and BI solutions. Provision Databricks and Unity Catalog with Terraform while enforcing GDPR, PII, governance, and security standards. Serve as technical design authority, mentor engineers, review code, and drive operational excellence.

Architect and build scalable batch and streaming data pipelines, cloud data warehouses, lakehouse infrastructure, feature stores, and AI-ready data workflows. The role emphasizes Python, SQL, Scala, dbt, Snowflake, Airflow, testing, observability, governance, CI/CD, Terraform, and agentic workflow development. You will collaborate with marketing, game, product, and analytics teams to deliver reliable, high-impact data solutions.

Graduate software developer role focused on building customer-facing applications, document processing solutions, and monitoring capabilities on SAP BTP. Responsibilities include developing RAP- and CAP-based services, integrating with SAP BTP Integration Suite, supporting authentication and authorization, configuring low-code solutions, exploring AI use cases, testing, documentation, and participating in agile ceremonies. The role provides mentorship and progressive responsibility in implementation and solution design.

Supports software quality assurance and testing for SAP BTP cloud applications, including test planning, test case creation, environment setup, integration and regression testing, defect tracking, and validation of functional, performance, accessibility, privacy, and security requirements. The role collaborates with developers and product teams in an agile environment while developing expertise in SAP technologies, test automation, and quality engineering.

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.

Build and operate production data and machine learning systems for algorithmic pricing. Responsibilities include designing scalable pipelines, enabling model training and deployment, productionizing models, monitoring performance, managing Databricks and Kubernetes workloads, creating dashboards and alerts, and translating business requirements into reliable software. The role also emphasizes testing, code review, CI/CD, observability, system reliability, and collaboration with scientists, product managers, engineers, and commercial stakeholders.

Build and operate production data pipelines, Python services, APIs, database systems, monitoring, tests, and data quality checks. Work across batch and near-real-time workloads while improving the performance, reliability, and cost efficiency of a large data warehouse. Own ambiguous engineering problems from concept through production, using Python, SQL, and databases such as PostgreSQL or MongoDB.

Own and build the company’s end-to-end data platform, including lakehouse architecture, ingestion, storage, orchestration, governance, and near-real-time processing. Develop trusted commercial and logistics datasets for finance, sales, procurement, and logistics. Establish data quality monitoring, testing, and alerting, while creating the foundation for pricing, forecasting, and AI agents. Partner with the Head of Data and help establish engineering standards for a future data team.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

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.