Top Data Engineer Jobs in Berlin

Build, analyze, and maintain scalable data infrastructure and reliable data pipelines. Translate business challenges into data models, ETL processes, algorithms, and production code. Collaborate with international teams across the full development lifecycle, including prototyping, software development, optimization, and maintenance. Work with distributed data processing, databases, cloud services, workflow orchestration, and large datasets in an agile environment.

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.

Build data warehouses, cloud infrastructure, AI workflows, and scalable data products. Integrate data from multiple systems, enable organizational data access, assess AI use cases, and collaborate with analytics, product, and software engineering teams in an agile environment.

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.

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.

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 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.

Build and ship production data pipelines across batch and near-real-time workloads. Develop Python services and APIs, manage operational databases, improve warehouse performance, and implement testing, monitoring, and data quality checks. Own ambiguous data problems from concept through production while ensuring reliability, scalability, and cost efficiency.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Build and scale data, AI, and workflow automation solutions. Responsibilities include developing reliable data pipelines, connecting client systems, automating business processes, integrating LLMs, and ensuring data is accessible for decision-making. The role requires collaboration with engineers and clients in agile environments, pragmatic product thinking, and the ability to communicate technical trade-offs to both technical and executive audiences.

Leads tax data engineering by gathering requirements, designing and operating scalable ETL/ELT pipelines, developing data models and warehouse structures, and ensuring data quality and observability. Partners with Tax, Analytics, Data Science, and business teams to validate data sources, support new initiatives, document systems, and automate recurring tasks using AI-assisted tools. The role also contributes to project planning, data tooling decisions, issue resolution, and engineering best-practice sharing.

Build and improve Modash’s large-scale creator search system across retrieval, indexing, ranking, embeddings, relevance, and low-latency serving. Own initiatives end to end, from requirements and architecture through implementation, production deployment, measurement, and iteration. Develop multimodal search capabilities using image, video, text, audio, LLMs, and vector search. Collaborate with data, product, customers, and leadership while balancing quality, cost, latency, and reliability.

Build and operate resilient, large-scale backend and data collection systems managing 400M+ creator profiles and billions of data points. Design distributed services that handle API instability, rate limits, outages, and partial failures; optimize collection economics; and own architecture, implementation, deployment, observability, and production operations. Collaborate with data and product teams to improve freshness, reliability, coverage, and system performance.

Leads mechanical engineering design and technical delivery for complex data centre projects. Responsibilities include designing cooling, ventilation, automation and control systems; coordinating with electrical and multidisciplinary teams; producing and reviewing detailed designs; managing engineers, resources and deadlines; supporting client relationships; and applying building physics, CFD and sustainable design principles.

Own end-to-end data engineering for growth initiatives, including source integration, pipelines, models, metrics, data products, and self-service capabilities. Partner with Marketing, Analytics, and Engineering to support acquisition, conversion, engagement, and retention decisions. Lead system design and improve testing, observability, performance, reliability, and cost efficiency. Mentor teammates, influence technical standards, and maintain production data systems.

Own and scale Andercore’s Databricks lakehouse, including ingestion, storage, orchestration, governance, domain modeling, data quality, monitoring, and near-real-time processing. Build trusted datasets and data foundations for pricing, forecasting, and AI agents while partnering with business stakeholders. Establish engineering standards as the first dedicated data platform hire, with potential to lead a growing data engineering team.

Lead technical presales for Microsoft Purview and data security solutions. Advise customers on translating EU and German privacy regulations into technical controls, policies, and architectures. Deliver presentations, demonstrations, workshops, architecture sessions, and proofs of concept. Develop security strategies, overcome technical blockers, support partner engagements, and drive technical decisions through sales and deployment handoff.

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 end-to-end industrial software deployments, from defining use cases and onboarding machine and production data through validation, rollout, training, and adoption. Work directly with operators, plant managers, IT, and OT teams to solve production problems. Build KPIs, anomaly signals, analytical workflows, and root-cause analyses using Python, SQL, statistics, and time-series data. Lead customer projects, identify expansion opportunities, and feed field insights back into the product.

Assesses satellite imagery quality through manual review and automated tooling. Builds and maintains QGIS-based tools, Python scripts, and geospatial data pipelines; supports investigations into customer quality disputes; contributes to automated quality-assessment systems; and documents technical findings for engineering teams. The role combines optical remote sensing expertise, software development, visual QA, and technical communication in a hybrid Berlin office setting.

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.