Top Data Engineer Jobs in Germany

Build and maintain scalable, reliable data infrastructure and self-service features for ETL, orchestration, and real-time analytics. Optimize compute and costs, automate operations for high availability, and contribute to designing and implementing new platform services.

Design and implement security frameworks for data platforms (Snowflake, GCP, Azure); automate infrastructure and security via IaC; manage CI/CD (GitHub Actions/Runners); integrate and operate SIEM and detection tools; collaborate with cross-functional teams and vendors; document processes and ensure compliance.

Design and implement security frameworks for enterprise data platforms (Snowflake, GCP, Azure). Automate infrastructure provisioning and security configs with IaC/Terraform, manage CI/CD via GitHub Actions, integrate security tooling and SIEM for threat detection, and document operational workflows while collaborating with cross-functional teams and vendors.

Lead design and implementation of AI-enabled, LLM-based backend features for enterprise post-trade data products. Drive architecture, integrations, testing, CI/CD, observability, and engineering standards while mentoring team members and ensuring secure, maintainable delivery in a regulated fintech context.

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 and operate an AI-native gameplay analytics ecosystem: define tracking, storage, processing, and semantic layers; build AI-driven reporting and agentic workflows; ensure data correctness; generate product intelligence and set organization-wide agent standards.

Design, implement, and maintain Python backend services and CI/CD/GitOps pipelines on AWS/Kubernetes. Build and maintain data models (Avro, SQL), publish via Kafka and schema registry, create access-layer models in Snowflake, and coordinate between Data and Tech teams to align schemas, priorities, and timelines.

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.

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.

Support Special Operations customers by collecting, cleaning, integrating, analyzing, and visualizing multi-source data. Develop dashboards, trackers, and executive-level data products; present findings and recommendations to stakeholders. Translate mission requirements into repeatable data solutions that inform planning, assessment, and decision-making.

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.

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.

The DevOps Engineer will maintain application environments, ensure infrastructure availability, oversee deployment processes, and lead training sessions within the team.

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.

Leading and mentoring a data platform engineering team while overseeing platform architecture, development, and compliance with security and governance in a cloud environment.

Analyze oilfield and sensor data to optimize well and reservoir production, define operational strategies, collaborate with customers to deliver data-driven insights, and contribute to development and validation of flow sensors and data-driven measurement systems using reservoir simulation and machine learning.

Design, build, and maintain streaming ingestion pipelines and migrate SaaS pipelines to in-house infrastructure. Deliver resilient, idempotent data into Snowflake, improve AWS streaming stack, manage IaC with Terraform, monitor and troubleshoot production pipelines, and collaborate on safe data access for AI tooling.

Design, implement, and monitor data pipelines for Better Planner to supply ML models with timely, clean data. Support developers and analysts across countries, improve the shared data platform, identify data quality issues, and provide technical mentorship and best-practice guidance to the tribe.

Investigate and reproduce HW/SW issues in Geely vehicles, collect and analyze vehicle logs (CAN/FR/LIN, DHU, TCAM), update and manage JIRA tickets, handle warranty parts, perform root-cause analysis, and coordinate with engineering teams to improve vehicle reliability.

Investigate and reproduce hardware and software issues in Geely vehicles, collect and analyze vehicle logs (CAN/FR/LIN, DHU, TCAM), manage and update JIRA tickets, handle warranty parts, perform root-cause analysis, and coordinate with engineering teams to improve vehicle quality.

Own, design, and develop the AI Data Platform: maintain a high-scale data product platform (≈400K pipeline jobs/month, petabyte scale), architect cross-cloud data infrastructure, build and operate automated data pipelines, ensure data observability and quality, and implement AI-driven features and AI data agents to expose AI-ready data products.

Lead design and delivery of scalable, secure Snowflake-based data platforms. Define architecture standards, build DBT ETL/ELT pipelines, enforce governance, ensure GDPR and pharma compliance, implement CI/CD/DataOps, optimize performance, mentor teams, and collaborate with global stakeholders for analytics and clinical insights.

Design and implement ontology-driven data integrations and secure pipelines across Palantir Foundry, MSS, Vantage, and cArmy. Build AI-driven automation agents, operational dashboards, GIS visualizations, and real-time tracking. Support cross‑enclave data management, data governance, validation, documentation, training, and IL6 SIPR/NIPR deployments to modernize counterintelligence analytic workflows.

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.