Top Data Engineer Jobs in Berlin

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Build and operate end-to-end data solutions for business-critical use cases, including Snowflake ingestion, Trino and Metabase exposure, automation, data quality, freshness, testing, SLAs, and incident resolution. Maintain the organization’s open-source data platform stack, enable self-service analytics, support data consumers, and translate business needs into reliable production solutions.

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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

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.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

Build and operate scalable batch and streaming pipelines for multimodal fleet and simulation data. Develop data models, catalogs, indexes, query capabilities, reproducible datasets, and quality validation workflows. Establish standards for lineage, observability, access controls, retention, governance, and cost management. Partner with autonomy, ML, simulation, and safety teams to define schemas, APIs, and data contracts while improving large-scale storage and compute reliability.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Build and maintain Azure-based data pipelines, warehouses, Data Vault and dimensional data marts using ADF, Azure SQL, and T-SQL. Develop Power BI datasets, semantic models, and reports; optimize stored procedures; investigate source systems; and implement data quality monitoring. The role also involves profiling datasets, validating business results, debugging data platforms, and collaborating with clients and a remote European team.

Build and maintain scalable data pipelines, ETL workflows, data models, dashboards, reports, and visualizations supporting game analytics, live operations, business intelligence, and reporting. Integrate multiple data sources, optimize SQL queries, ensure data quality, monitor the data platform, and collaborate with cross-functional teams to deliver reliable and actionable data solutions.

Build and maintain scalable data pipelines, ETL workflows, data models, and infrastructure supporting game analytics, live operations, reporting, and business intelligence. Develop dashboards and visualizations, integrate and validate data from multiple sources, optimize SQL queries, monitor data platforms and reporting systems, and collaborate with cross-functional teams to deliver reliable, actionable data solutions.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

Own and evolve N26’s core dbt services, including CI/CD, performance, quality standards, and support for analytics engineering teams. Build Python tooling and data governance infrastructure for anomaly detection, freshness monitoring, drift detection, and data contracts. Improve shared SQL and data modeling practices, guide the analytics engineering community, and collaborate with the data platform team on scalable infrastructure and technical direction.

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.

Build and ship agentic AI systems for reward design and other data-team problems. Develop agents, LLM pipelines, RAG systems, retrieval and evaluation workflows, and determine where AI can deliver business impact. Work closely with data scientists, the CTO, and leadership, using Python and large behavioral datasets across cloud infrastructure. Establish the company’s AI engineering patterns, tooling, and standards.

Build scalable data warehouses, cloud infrastructure, AI workflows, and data-driven applications. Integrate data from multiple systems, ensure accessible and consistent data, evaluate AI use cases, and collaborate with analytics, product, and software engineering teams in an agile environment.

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 and own backend data-ingestion systems that connect external plant data to machine-learning products. Responsibilities include developing APIs and services, working with PostgreSQL, deploying and monitoring production systems in AWS and Kubernetes, troubleshooting upstream data issues, investigating anomalies, and explaining findings to customers. The role involves close collaboration with customers, partners, sales, customer success, and product teams while contributing across the full software delivery lifecycle.

Own and improve the company’s data platform, including scrapers, integrations, event-driven pipelines, storage, monitoring, backend services, infrastructure, security, and CI/CD. Build reliable systems handling millions of records, ensure data quality and freshness, simplify existing architecture, document systems, communicate trade-offs, and guide junior engineers. The role requires hands-on ownership of production systems in a fast-moving company.

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.