Data Analyst

Winton Group; Greater London; On-site; GBP 50,000 - 70,000; Full time

Job summary

Winton Group is seeking a Data Analyst to oversee the quality and consistency of datasets within our Quantitative Platform team. This role involves defining acceptance criteria for new data products and acting as a subject-matter expert to resolve data anomalies.

Candidates should have over 3 years of experience working with financial data and demonstrate strong analytical skills using Python. A collaborative mindset and attention to detail are essential in this fast-paced environment.

Qualifications

• 3+ years’ experience working with financial data vendors and their products.
• Strong grasp of cross-asset class time series data.
• Hands-on analytical experience using Python.
• Meticulous attention to detail.

Responsibilities

• Define and execute acceptance criteria for new data products.
• Act as a subject-matter expert on data products.
• Build and maintain an automated catalogue of datasets.
• Ensure datasets meet high data quality standards.

Skills

Experience with financial data vendors

Cross-asset class time series data understanding

Analytical skills using Python

Attention to detail

Communication and collaboration skills

Job description

Winton leverages quantitative analysis and cutting‑edge technology to identify and capitalize on opportunities across global financial markets. We foster a collaborative and intellectually stimulating environment, bringing together individuals with Mathematics, Physics and Computer Science backgrounds who are passionate about applying rigorous scientific methods to financial challenges. As a fundamentally data‑driven business, our success is heavily linked to the acquisition, processing, and analysis of vast datasets. High‑quality, well‑managed data forms the critical foundation for our quantitative research, strategy development, and automated trading systems.

As a Data Analyst within our Quantitative Platform team, you will own the quality, consistency, and discoverability of datasets as they move from onboarding into production. You will uphold our data standards and catalogue, so datasets are easy to find, trust, and use. Your work spans vendor‑sourced financial data, time series across instruments and asset classes, and complex, multi‑table products where correct mapping and definitions matter as much as raw data accuracy.

Responsibilities

• Defining and executing rigorous acceptance criteria for new and evolving data products. From sample evaluation through to production, including coverage analysis, staleness and gap detection, and reconciliation against trusted references where available.
• Acting as a subject‑matter expert on our data products, helping Strategy Managers with vendor formats, data anomalies, corporate actions semantics, identifiers, and documentation gaps; escalating and tracking issues with vendors and internal stakeholders until resolved.
• Building and maintaining an automated catalogue of datasets (descriptions, owners, refresh cadence, SLAs, source systems, schemas, known limitations). Keeping the catalogue aligned with reality when pipelines change so consumers rely on current metadata.
• Systematically probing new and existing datasets to ensure they meet our high data quality standards. Stress‑testing point‑in‑time, versioning and revision semantics; chasing down corrections, duplicates, staleness, and discontinuities with source vendors.
• Contributing to data quality frameworks, onboarding checklists, and documentation (data dictionaries, lineage notes, known limitations) so quality expectations are repeatable and auditable.
• Partnering with Data Engineers on handoff contracts (schemas, SLA expectations, alerting thresholds), with Quant Researchers on analytic sanity checks, and with operations on repeatable triage when anomalies appear in production datasets.

Qualifications

• 3+ years’ experience working with financial data vendors and their products.
• Strong grasp of cross‑asset class time series data and what common or nuanced issues can arise when onboarding new datasets.
• Comfort with complex, multi‑entity datasets (join keys, slow‑changing dimensions, snapshots vs history) and a methodical approach to debugging inconsistencies.
• Hands‑on analytical experience using Python, and the ability to summarize findings clearly for both technical and non‑technical audiences.
• Meticulous attention to detail and a bias toward evidence‑based conclusions.
• Excellent communication and collaboration skills, and the ability to work in a team in a fast‑moving, data‑centric environment.

Advantageous

• Direct experience with reference and hierarchical data (security masters, classification trees, entity relationships) and cross‑vendor alignment.
• Familiarity with market, fundamental, or alternative datasets used in systematic or quantitative investment workflows.
• Exposure to data quality tooling or statistical monitoring (distributions, drift, anomaly detection) applied to production or near‑production feeds.
• Practical experience using LLMs to accelerate complex data investigations.

Equal Opportunity Workplace

We are proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.