{"@context":"https://schema.org/","@type":"JobPosting","title":"Data Analyst

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

• 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.