About this role
\nWhat your work will look like:
- 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.
- 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.
Nice to have:
- Infrastructure as Code (Terraform, CloudFormation).
- Event-driven architecture (Kafka or similar).
- Additional languages used in ingestion or operational repos, such as Go.
- Financial services or payments data pipelines experience.
- Search or indexing experience, Avro, or schema registries.
Please note that we will not sponsor your visa or relocation, and you must have a valid work permit to be eligible for this position.
What’s next? - 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
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