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# Data Analyst

- **Company:** Winton
- **Location:** London, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Decision Tree Learning, Data Dictionary, Data Governance, Extract Transform Load (ETL), Software Debugging, Python (Programming Language), Management of Software Versions, Large Language Models, Data Pipelines
- **Published:** August 17, 2026
- **Apply:** https://www.efinancialcareers.com/jobs-United_Kingdom-London-Data_Analyst.id24596982

## About the Role

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; escalate and track 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 probe new and existing datasets to ensure they meet our high data quality standards. Stress-test point-in-time, versioning and revision semantics; chase 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. What we are looking for: 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. What would be 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. Experience building ETL/ELT pipelines using Python. 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.



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