At a Glance
- Tasks: Own data integrity and improve pipelines in a dynamic finance environment.
- Company: Join a leading quantitative finance firm with a focus on innovation.
- Benefits: Competitive pay, 35 days leave, free lunch, and a relaxed dress code.
- Other info: Enjoy a collaborative culture with excellent career growth opportunities.
- Why this job: Make an impact by shaping tools that drive high-impact research.
- Qualifications: Strong SQL and Python skills; financial markets knowledge is a plus.
The predicted salary is between 70000 - 90000 £ per year.
We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity. From our London HQ, we unite world‑class researchers and engineers in an environment that values deep exploration and methodical execution – because the best ideas take time to evolve. Together we’re building a world‑class platform to amplify our teams’ most powerful ideas.
As part of our engineering team, you’ll shape the platforms and tools that drive high‑impact research – designing systems that scale, accelerate discovery and support innovation across the firm.
The role Reference Data underpins G-Research’s trading and research workflows by curating core market datasets. As a Data Analyst, you will own day‑to‑day data integrity and investigate anomalies end to end. You will partner with engineers and stakeholders to improve pipelines, controls and downstream usability. This is a highly stakeholder‑facing role requiring proactive communication, accountability and strong teamwork.
Responsibilities
- Owning daily data quality across reference data products including instruments, corporate actions, calendars, pricing and identifiers.
- Designing and maintaining data quality checks, dashboards and alerting.
- Leading root cause analysis on incidents including trace lineage, identifying failure modes and driving remediation with engineering.
- Communicating proactively with stakeholders, setting expectations and explaining impact, severity and timelines.
- Driving issues to closure through clear ownership, tracking actions, validating fixes and preventing regressions.
- Contributing as a strong team player by documenting learnings, sharing context and improving team standards.
Qualifications
- Strong SQL skills.
- Strong Python skills for dataframe analytics using pandas or polars.
- Confident use of AI coding assistants with sound judgment and validation.
- Financial markets domain knowledge.
- Collaborative approach to working with stakeholders.
- Automation‑first mindset to reduce RTB and eliminate recurring manual work.
Nice to have:
- Familiarity with Git, notebooks and reproducible analytics practices.
- Experience working with engineers in modern deployment environments such as containers or Kubernetes.
Benefits
- Highly competitive compensation plus annual discretionary bonus.
- Lunch provided (via Just Eat for Business) and dedicated barista bar.
- 35 days’ annual leave.
- 9% company pension contributions.
- Informal dress code and excellent work/life balance.
- Comprehensive healthcare and life assurance.
- Cycle‑to‑work scheme.
- Monthly company events.
Inclusivity
G‑Research is committed to cultivating and preserving an inclusive work environment. We want to ensure that applicants receive a recruitment experience that enables them to perform at their best. If you have a disability or special need that requires accommodation, please let us know.
Data Analyst in London employer: Barlowe LLP
At G-Research, we pride ourselves on being an exceptional employer, offering a dynamic work environment in the heart of London where innovation thrives. Our commitment to employee growth is evident through our collaborative culture, competitive compensation, and extensive benefits, including 35 days of annual leave and a strong focus on work/life balance. Join us to be part of a team that values your contributions and supports your professional development while tackling complex challenges in quantitative finance.
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We think you need these skills to ace Data Analyst in London
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