Data Platform Engineer – Inventory & Observability

Quberesearchandtechnologies; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Qube Research & Technologies (QRT) is expanding its infrastructure data platform to deliver accurate, scalable data foundations across resources, ownership, and performance. This role focuses on building production-grade data models and end-to-end data pipelines for inventory and observability.

You will collaborate with infrastructure, security, engineering and observability teams to onboard sources, ensure data quality, lineage and timely delivery, shaping the platform for scale.

Qualifications

• Experience building and operating production-grade data platforms and large-scale data-processing apps.
• Strong SQL and dimensional/analytical data modelling for maintainable and performant schemas.
• Solid Python engineering with tested production-grade ETL/ELT pipelines.
• Proven data correctness through provenance, reconciliation and source-of-truth reasoning.
• Familiarity with infrastructure/observability concepts and cross-domain collaboration.
• Experience with production pipelines, diagnostics of data/performance issues.
• Knowledge graphs, ontologies, taxonomies and modelling heterogeneous data.
• Automation bias: balancing correctness, performance and delivery.

Responsibilities

• Design and build data models for infrastructure and operational data.
• Integrate inventory, configuration, security and observability data from multiple systems.
• Ensure consistent counts, data lineage and ownership across consumers.
• Create fast-queryable data structures for scale and reasoning.
• Automate data processing, validation and delivery; monitor data quality and freshness.
• Collaborate with infra, security, engineering and observability teams to onboard sources.
• Take ownership of end-to-end data platforms and adapt to changing tech.

Skills

SQL

Python

Data modelling

ETL/ELT

Observability

Data quality

dbt

AWS

Linux

GitLab CI/CD

Superset

Tools

dbt

Superset

GitLab CI/CD

AWS

Job description

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high-quality returns for our investors.

As QRT’s infrastructure estate grows, maintaining accurate data on its resources, ownership and performance is increasingly important. In this hands‑on role, you will build and operate the data foundations underpinning our infrastructure inventory and observability capabilities. The focus extends beyond dashboard design to creating an accurate, consistent and reliable view of the platform, its ownership and its behaviour.

Your future role within QRT:

• Design and build data models for infrastructure and operational data, defining canonical representations for resources, ownership, capacity and utilisation.
• Integrate inventory, configuration, security and observability data from multiple systems into a coherent, queryable view.
• Keep key counts and metrics consistent across different consumers, and make data lineage, ownership and limitations explicit.
• Design data structures that are fast to query and easy to reason about at the scale of QRT’s estate.
• Automate data processing, validation and delivery, and build the tooling to detect data‑quality, freshness and performance issues before consumers do.
• Investigate and resolve data‑quality, freshness and performance issues, solving root causes rather than symptoms.
• Collaborate closely with infrastructure, security, engineering and observability teams to onboard new sources and improve the accuracy of the platform view.
• Take ownership of systems end‑to‑end, and adapt the platform as scale, requirements and underlying technologies change.

Your present skillset:

• Experience building and operating production‑grade data platforms, analytical data models, or large-scale data‑processing applications.
• Strong SQL and dimensional or analytical data modelling experience, including the design of schemas that are maintainable, performant and easy for consumers to reason about.
• Strong Python software engineering experience, including the design of maintainable, tested production applications and scalable ETL/ELT pipelines.
• Demonstrated rigour around data correctness: establishing provenance, reconciling conflicting or incomplete sources, and reasoning carefully about identity, ownership and source of truth.
• Solid working understanding of infrastructure and observability concepts, and comfort working across data, infrastructure and observability domains.
• Experience operating business‑critical pipelines and diagnosing data, performance and reliability issues in production.
• Familiarity with knowledge graphs, ontologies and taxonomies, and their application to modelling heterogeneous operational data.
• A strong bias towards automating repetitive work, and pragmatism in balancing correctness, performance and delivery.
• Excellent problem‑solving and communication skills, and the ability to own outcomes across research, engineering and infrastructure teams.
• Preferred:
  • A background in data science, statistics or applied machine learning, particularly applied to incomplete, noisy or inconsistent data, or to inferring missing information and identifying unusual behaviour.
  • Experience with SQL-based analytical platforms, dbt, object storage and data catalogues.
  • Experience with infrastructure inventory or CMDB data, ownership and capacity/utilisation modelling.
  • Experience with metrics, logs and vulnerability data, and with observability tooling.
  • Experience with Superset or comparable BI and data‑exploration tools.
  • Experience with Linux, containers and GitLab CI/CD.
  • Experience with AWS services and cloud SDKs.

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work‑life balance.