Top Data Engineer Jobs in Berlin

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.

Develop and maintain scalable data engineering features, workflows, pipelines, and cloud-based architectures for quick commerce operations. The role involves pipeline maintenance, architecture evolution, engineering best practices, CI/CD, and collaboration with data scientists on machine learning lifecycle management, monitoring, alerting, and model deployment. Candidates need strong Python, cloud, containerization, orchestration, database, and scalable systems expertise.

Design and operate a governed data platform for contract-driven publishing and consumption. Build PostgreSQL-to-Debezium-to-Kafka CDC workflows, GitOps-style contract validation and reconciliation, developer tooling, Kubernetes runtimes, and DataHub catalog integrations. Define data product standards covering schemas, ownership, lifecycle, compatibility, lineage, access, and recovery. Collaborate with infrastructure, product, analytics, data engineering, and AI/ML teams to deliver reliable, reusable production data capabilities.

Build and maintain reliable data models and ETL/ELT pipelines across AWS-based data lake layers. Ingest high-volume billing, CRM, network, and customer data; process Kafka events; develop dbt transformations; orchestrate workflows with Airflow; and implement data quality, monitoring, documentation, and reconciliation practices. Collaborate with stakeholders and engineers to deliver accurate, timely, traceable datasets for reporting and self-service analytics.

The Data Engineer will manage tracking data pipelines, ensure data quality and governance, optimize ELT processes, and collaborate with teams to enhance data accessibility and reliability.

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

Build and operate a scalable AWS-based data platform and lakehouse. Develop and maintain data pipelines, ingest internal and third-party data, prepare/clean data, create data models for reporting, analytics and AI, and implement testing and monitoring. Collaborate with product and engineering teams and share best practices.

Build scalable backend functionality, algorithms, and data pipelines for industrial IoT products. Responsibilities include optimizing microservice architecture, collaborating with frontend and backend engineers, participating in architecture through testing and maintenance, and evaluating emerging technologies. The role requires modern API development, serverless and streaming architectures, SQL and NoSQL databases, Linux, and backend DevOps technologies.

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.

Own and build data ingestion and cleaning pipelines for plant data, maintain APIs and PostgreSQL databases, support ML pipelines, debug anomalies with customers, and improve onboarding with AI-powered ingestion.

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.

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.

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

Design, build and maintain scalable data pipelines and BI infrastructure. Ensure secure, high-quality data availability through modelling, performance tuning, governance, and collaboration with analysts and stakeholders. Mentor peers, review code, and introduce new tools and best practices.

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.

As a Data Engineer, you will design and build data pipelines, models, and infrastructure for analytics and machine learning while ensuring data quality and compliance.

Design, implement, scale, and operate Python APIs, backend services, and end-to-end data pipelines on cloud platforms. Build event-driven real-time/asynchronous processing, apply data modeling and database design, enforce CI/CD and testing, drive observability and performance, and own services from schema and API design to deployment and monitoring.

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.

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 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.

Build and own ABOUT YOU’s modern data platform, including Dagster, dbt, AWS ECS, and Google BigQuery infrastructure. Maintain high-volume ELT pipelines moving terabytes of data from MySQL to BigQuery while protecting source-system performance. Deliver critical executive reports and develop tooling for monitoring, data quality, connectors, cost control, testing, CI/CD, and cataloging. Collaborate pragmatically with engineering and business stakeholders using strong software engineering, SQL, Python, and communication skills.

Own and evolve the company’s data platform using Dagster, dbt, AWS ECS, and Google BigQuery. Build foundational tooling, monitoring, data checks, connectors, testing, CI/CD, cataloging, and cost controls. Maintain high-volume ELT pipelines moving terabytes from AWS MySQL to BigQuery while protecting source-system performance. Deliver critical executive reports and enable other data teams through reliable, scalable data infrastructure.

Build and operate scalable batch and streaming data solutions supporting fraud prevention. Develop point-in-time-correct datasets, historization, data quality controls, observability tooling, and reliable pipelines for model training, inference, backtesting, and fraud-rule evaluation. Partner with Data Science and Fraud Operations teams to deliver resilient data products and maintain consistency between batch and real-time processing.