Top Data Engineer Jobs in Hamburg

Build and maintain ML-scale data infrastructure: feature store, real-time and batch pipelines, data quality and validation, and low-latency systems. Translate Data Science needs into high-performance Flink streams, eliminate ETL bottlenecks, collaborate on online ML systems, and implement monitoring, governance, and deployment processes for production ML workflows.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Designs and maintains data management architectures for irregular warfare and operations data, including ingestion, cataloging, metadata, retrieval, dissemination, dashboards, and Common Operating Pictures. Supports knowledge management frameworks, cross-command information sharing, data governance, secure cloud environments, and operational decision-making. Collaborates with analysts, planners, and leadership while troubleshooting complex data flows and architectures in classified NIPR, SIPR, and JWICS environments.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

Lead design and ownership of a cloud-native data platform: build scalable batch ETL and ingestion pipelines, develop analytical models (Athena/Redshift/ClickHouse), integrate data workflows with microservices on ECS, ensure data quality/observability, performance tuning, implement IaC and CI/CD, and mentor engineers.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

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.

Design, build, and operate cloud-native, serverless ClickHouse infrastructure. Develop and maintain Kubernetes operators, improve metrics pipelines, support data ingestion/transformation systems (Spark/Kafka), and collaborate with core database and data plane teams to ensure scalable, highly available distributed systems.

Lead design and implementation of customer-facing data intelligence applications: build shared API layers, low-latency data-access and caching strategies, advanced interactive visualizations, LLM-enabled interfaces, and mentor engineers while shaping architecture and standards.

Own and evolve the company data platform (Dagster, dbt, GBQ, AWS ECS); build tooling, monitoring, CI/CD and connectors; maintain high-volume ELT pipelines moving terabytes from MySQL to BigQuery; and deliver core executive reports used across teams.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Own and build the internal data warehouse and analytics platform: consolidate product and operational data, define data models and contracts, create transformation pipelines, enable AI/ML datasets, implement data quality, lineage, and observability, and collaborate with stakeholders to support BI and analytics.

Lead and operate production-grade data pipelines and streaming systems to ingest, enrich, process, and serve web event and intent data at scale. Own cloud infrastructure as code, observability, data quality, and CI/CD. Collaborate with product and ML teams to deliver reliable, low-latency data powering customer-facing features and AI/ML workflows.

Lead design and implementation of scalable, real-time data infrastructure. Build and optimize production-grade streaming pipelines, employ spec-driven development and agentic AI workflows, implement testing/monitoring, take ownership of systems, and mentor junior engineers.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

The Technical Privacy Expert will implement privacy-by-design principles, ensure compliance with regulations, mentor teams, and develop technical frameworks for privacy in technologies.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.