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# Data Engineer

- **Company:** Qube Research & Technologies (qrt)
- **Location:** Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Apache HTTP Server, Big Data, C++ (Programming Language), Cloud Computing, Software Quality, Database Queries, Linux, Distributed Computing Environment, Identity and Access Management, Python (Programming Language), Workflow Management Systems, Parquet, Apache Spark, Cloudformation, Kubernetes, Dask, Data Management, Cloudwatch, Terraform, Docker
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/senior-data-engineer-qube-research-technologies-8740748

## About the Role

* 5+ years of experience building production-grade data platforms or large-scale data processing systems.
 * Strong Python software engineering experience.
 * Hands-on experience with Apache Airflow or similar workflow orchestration platforms.
 * Experience with AWS services including S3, EC2, AWS Batch, IAM, CloudWatch, and cloud SDKs.
 * Strong understanding of data quality, validation, testing, monitoring, and observability.
 * Good understanding of distributed data processing concepts.
 * Strong Linux skills and experience developing and supporting production systems.
 * Excellent problem-solving skills with the ability to collaborate effectively across research, engineering, and infrastructure teams.

Nice to have:

 * Experience working with market or financial data.
 * Knowledge of non-equity asset classes (e.g. futures, fixed income, commodities, FX).
 * C++ development experience.
 * SQL proficiency.
 * Experience with Docker, Kubernetes, and Infrastructure-as-Code (Terraform or CloudFormation).
 * Familiarity with Apache Parquet, Apache Arrow, or distributed processing frameworks such as Spark, Ray, or Dask.

## Description

* Build and operate scalable, automated data pipelines using technologies such as Apache Airflow and AWS Batch across AWS and QRT's compute infrastructure.
 * Ensure data quality, validation, reconciliation, and observability across critical production pipelines.
 * Design systems that efficiently process large-scale datasets while supporting compute-intensive research workloads.
 * Collaborate closely with quantitative researchers, software engineers, and infrastructure teams to accelerate dataset onboarding and improve research productivity.
 * Drive engineering best practices with a strong focus on software quality, automation, scalability, and operational excellence.


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