Top Data Engineer Jobs in Hamburg

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.

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.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Lead the architecture and development of Core Data Services for customer-facing applications on SAP BTP and SAP RISE. Design modular services using RAP and CAP, integrate through SAP BTP Integration Suite, and implement authentication and authorization with XSUAA. Provide technical leadership through architecture decisions, code reviews, mentoring, release governance, documentation, and deployment readiness. Contribute to AI-enabled solutions and deliver secure, scalable applications for regulated financial services environments.

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.

Owns supply data solutions by building reliable analytics datasets, reusable data marts, monitored Python and GCP pipelines, documentation, data governance, access controls, and data quality checks. Partners with scientists, analysts, and supply stakeholders to establish data contracts and improve platform reliability. The role also mentors junior analysts and promotes SQL best practices.

Develop optimization and algorithmic solutions, build reliable AWS infrastructure and orchestration pipelines, write production-grade Python, manage CI/CD and CloudFormation deployments, monitor system health, and mentor junior engineers. The role contributes to redesigning a pricing engine that integrates optimization and forecasting models for e-commerce pricing, while translating business requirements into scalable software solutions.

Own and optimize high-throughput batch and streaming data pipelines, distributed Apache HBase storage, and production infrastructure for maritime telemetry and predictive analytics. Operate containerized AWS environments using Kubernetes, Docker, Terraform, and GitOps; establish reliability metrics and operational SLAs. Collaborate with data science, product, and SRE teams, mentor engineers, and promote strong code review and AI-assisted development practices.

Own and evolve the company’s data platform, including Dagster, dbt, AWS ECS, and BigQuery. Build tooling, monitoring, testing, connectors, and CI/CD capabilities; maintain high-volume ELT pipelines moving terabytes from MySQL to BigQuery; and deliver critical executive reports. Apply strong Python, SQL, software engineering, infrastructure, and data platform expertise while collaborating pragmatically with engineering and business stakeholders.

Build and own the company’s modern data platform, including Dagster, dbt, AWS ECS, and BigQuery infrastructure. Develop tooling for monitoring, data quality, connectors, cost control, testing, CI/CD, and cataloging. Maintain high-volume ELT pipelines and critical executive reports while improving reliability and efficiency for data teams. Apply strong software engineering practices, communicate effectively with stakeholders, and balance pragmatic delivery with technical excellence.

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.

Lead development of Data and AI features from data architecture to optimization and simulation. Design scalable data architectures, reliable pipelines, and DataOps/DevOps workflows. Partner with analysts, product managers and supply chain specialists to turn complex logistics data into actionable solutions.

Design and maintain high-throughput data replication pipelines connecting platform systems with reporting tools. Evaluate storage engines and data models across MySQL, OpenSearch, BigQuery, and ClickHouse, modernize legacy processing architecture, and establish scalable information structures. Drive monitoring, reliability, operational excellence, and secure migrations while collaborating with backend and analytics engineers and setting technical standards.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

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.

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.

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.