Senior Data Engineer, Unzer

• You have 5+ years of data engineering or related experience, with production-grade pipeline development primarily in Python
• You have excellent knowledge of SQL and a strong understanding of data warehouse concepts, with hands-on experience in Redshift (or similar) for modeling, ingestion, performance optimization, and data integrity
• You have hands-on experience with ELT/ETL flows using dbt and workflow orchestration with Apache Airflow, including DAG design, operators, and troubleshooting
• You have solid AWS experience across object storage, containers, managed ETL, serverless, data warehousing, replication (for example, DMS), IAM, and related analytics services such as Athena and Glue
• You have experience with Git, CI/CD, and Docker, and can maintain deployment pipelines and manage container image lifecycle
• You have a proven ability to own projects from requirements gathering through production monitoring and incident response
• You have a strong operational mindset and can troubleshoot systematically across a distributed, multi-repository platform
• You can work independently while also collaborating well with analytics and platform stakeholders

Desirable

• The Data Team at Unzer is responsible for the end-to-end health and evolution of our data platform.
• We build and operate a multi-repository, distributed stack spanning orchestration, transformation, lake ingestion, and infrastructure configuration.
• Our processing patterns range from batch to near real-time, with a strong emphasis on availability, data quality, and operational robustness in a 24/7 production environment.
• We like having the freedom to pick the right tooling and approach for each use case, and our goal is to make data easy to access and understand across the company, so people can actually make decisions based on it
• Own data pipelines end to end: design, build, run, monitor, and troubleshoot across orchestration, transformation, lake ingestion, and cloud infrastructure
• Build and maintain Apache Airflow workflows to schedule transformation jobs, ingestion tasks, and containerized workloads
• Develop dbt models in Redshift (or a similar warehouse), including incremental strategies, data quality tests, and CI validation, and coordinate model selection and run parameters with orchestration
• Design and build lake-layer pipelines (bronze, silver, gold, or your platform's equivalent), including replication, ETL/ELT jobs, and operational workloads for event-driven and near real-time use cases
• Implement workloads as containerized services or serverless functions depending on what fits best, and manage batch jobs, platform monitoring workflows, and container image lifecycle
• Manage cloud infrastructure and configuration: compute clusters, object storage, IAM, the data warehouse, networking, parameter stores, secrets, and access and grant management
• Maintain CI/CD pipelines (for example, GitHub Actions) with secure cloud authentication, and own the flow from test to production
• Investigate and resolve production incidents, including orchestration failures, transformation run issues, serverless errors, replication lag, performance bottlenecks, and data quality problems, using monitoring and alerting effectively
• Follow the typical new source flow end to end: configure replication or ingestion, land the data in object storage, build the consumer or transformation layer, wire up the compute, orchestrate it, model it in the warehouse, and grant access

• Does it sound exciting? - Apply with your CV in English. Please don’t shy away if you don’t meet all the requirements! We’re looking forward to meeting you
• The interview process includes a 30-minute call with the Talent Acquisition Manager, a take-home assignment, an on-site technical interview with the team, and a 45-minute call with the Hiring Manager