Data Analyst – HSBC

Contract Length – until 31/12/26

Rate – up to £570/Umbrella

Location – Hybrid 3 days in Sheffield

Role purpose

Deliver practical, decision-ready insights by sourcing data from multiple technologies, applying fit-for-purpose analytical tools, and communicating clear conclusions and recommendations.

Key responsibilities

• Source and shape data (end-to-end): Identify, access, extract, and combine data from multiple platforms (e.g., databases, APIs, cloud services, spreadsheets, logs, BI tools).
• Data preparation and quality: Clean, validate, reconcile, and document datasets; highlight limitations, assumptions, and data quality risks early.
• Practical analysis: Apply the right analytical approach (descriptive, diagnostic, trend, segmentation, cohort, funnel, root-cause) to answer business questions quickly and accurately.
• Tool selection and automation: Select fit-for-purpose tools (SQL, Python/R, Excel, BigQuery, Alteryx, etc.) and automate repeatable workflows where it saves time and reduces error.
• Insight storytelling: Build clear dashboards, reports, and presentations that explain the “so what” and “now what” for stakeholders at different levels.
• Strong conclusions and recommendations: Translate analysis into actionable recommendations, quantify impact where possible, and propose next steps and experiments.
• Stakeholder partnership: Clarify requirements, challenge ambiguous asks, and align on definitions/metrics to avoid “multiple versions of the truth.”
• Governance and controls: Handle data responsibly, follow relevant data privacy/security requirements, and maintain reproducible analysis (versioning, documentation, auditability).

Required Skills And Experience

• Proven experience in data analysis/analytics in a practical, delivery-focused environment.
• Strong data sourcing capability across varied technologies (relational databases, files, APIs, cloud data stores, BI semantic layers).
• Excellent SQL and solid capability in at least one analytical language (Python or R).
• Strong data visualisation and communication skills (e.g., Qlik, Jupyter Notebooks) with an ability to tailor messages to the audience.
• Demonstrated ability to choose the right tool for the job and explain trade-offs (speed vs. robustness, one-off vs. scalable).
• Sound understanding of data quality, metric definitions, and basic statistical concepts (sampling, bias, confidence, correlation vs causation).
• Strong written and verbal communication—able to present findings, defend methodology, and drive decisions.

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