Top Data Engineer Jobs in Hamburg

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.

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.

Zuhlke is seeking an Expert Data Engineer focused on Databricks to support client transformation initiatives. The provided description contains limited role-specific responsibilities, qualifications, or project details. The position appears aligned with data engineering and the development of modern data solutions within Zuhlke’s multidisciplinary engineering and innovation teams.

Develop and maintain BI dashboards, reports, data models, and ETL processes supporting commodity trading, risk, P&L, inventory, and logistics analysis. Integrate data from trading platforms, market providers, ERP systems, and warehouses. Automate reporting, improve data quality, monitor market indicators, and translate stakeholder requirements into scalable analytical solutions and visualizations.

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.

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.

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 and own a modern data platform using Dagster, dbt, AWS ECS, and BigQuery. Maintain high-volume, business-critical ELT pipelines and executive-level reporting. Develop tooling for monitoring, data quality checks, connectors, cost control, testing, CI/CD, and cataloging. Apply strong software engineering practices to data engineering while collaborating with diverse stakeholders and communicating clearly in English.

Own supply data by building reliable analytics datasets, reusable data marts, and monitored pipelines. Partner with analysts, data scientists, and supply stakeholders to establish data contracts and a shared source of truth. Responsibilities include Python and GCP pipeline development, SQL optimization, documentation, IAM and access governance, cost and performance monitoring, data quality checks, and mentoring junior analysts.

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.

Own and evolve data storage and warehouse foundations (schemas, migrations, performance, reliability). Design data models and marts for analytics, build backend services and RESTful APIs in Python, improve testing, monitoring, and reliability, and collaborate with product and engineering teams to deliver data-driven features.

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.

Designs and maintains high-throughput Reverse ETL pipelines connecting core platforms with reporting tools. Evaluates storage engines and data models across MySQL, OpenSearch, BigQuery, and ClickHouse; modernizes legacy processing architecture; and partners with engineering teams on scalable information architecture. The role also drives monitoring, reliability, maintainability, technical standards, and secure migration strategies for data infrastructure.

Build the end-to-end analytics foundation for a new mobile game, including gameplay tracking, data architecture, pipelines, semantic models, reporting, and AI-powered insight workflows. Use agentic engineering tools to accelerate development while owning correctness, quality gates, and production review. Analyze player behavior, monetization, attribution, anomalies, and trends to support product and leadership decisions. Establish organizational standards for AI-assisted development and independently drive technical initiatives from concept through production.

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.

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 and build large-scale distributed data systems for blockchain analytics. Translate ambiguous product requirements into technical designs, sequence work, make architectural decisions, and own components from design through production. Work across Go, Kotlin, Rust, and Python while optimizing data ingestion, distributed processing, and data lake storage. Mentor engineers, improve design reviews and testing practices, and collaborate effectively in a fully distributed team.

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.

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.

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.