Senior Data Analyst

Role Overview

Build and Maintain Analytical Models

• Design and maintain dimensional models and semantic layers in Power BI or an equivalent platform.
• Apply star schema, SCD, and medallion-layer patterns and resolve query folding, cardinality, and refresh bottlenecks.
• Define and document metric and KPI calculation logic, grain, and refresh cadence.
• Improve model quality, reusability, and maintainability within established architecture standards.

Investigate Operational Signals

• Investigate operational and customer signals across telemetry, crash logs, support records, incident data, and observability platforms.
• Write optimised SQL, Python, and KQL queries and create reusable scripts and templates.
• Apply anomaly detection, trend analysis, cohort analysis, and time-series methods.
• Support root-cause investigations with data-driven findings and recommendations.

Deliver BI Solutions

• Build and publish performant Power BI reports and dashboards aligned with BI deployment standards.
• Optimise DAX measures, report queries, and visual-level query performance.
• Maintain documentation for measure definitions, report logic, and data sources.
• Partner with Data Engineering to validate pipeline outputs and communicate analytical data requirements.

Collaborate Across Functions

• Work with Incident Management, Support, SRE, Software Development, and Data Engineering stakeholders.
• Communicate findings to engineers, domain experts, project managers, and business stakeholders.
• Help ensure data usage and retention practices align with legal and compliance requirements, including GDPR.

Experience and Qualifications

• 5–8 years of experience in a data-focused role such as Data Analyst, BI Developer, or Analytics Engineer.
• BE or B.Tech degree in Computer Science, Software Engineering, Data Science, or a related field.
• Production experience building analytical data models, including star schemas, semantic layers, and dimensional modelling.
• Experience delivering analytical solutions across multiple data sources in a software-driven environment.
• Experience with defined metrics frameworks and their documentation and improvement.

Technical Skills

• Strong SQL proficiency, including window functions, CTEs, query optimisation, and large datasets.
• Python proficiency for analytical work, including pandas, NumPy, and visualisation libraries.
• Hands-on Power BI experience with DAX, semantic models, performance tuning, and deployment.
• KQL proficiency for operational and telemetry data in Azure Monitor, Log Analytics, and Application Insights.
• Experience with Databricks or an equivalent platform, including Delta tables and notebooks.
• Understanding of statistical techniques, distributed tracing concepts, data privacy, and GDPR.

Personal Competencies

• Question data quality and verify findings before presenting them.
• Take ownership from problem scoping through delivery of reliable analytical solutions.
• Communicate clearly with technical and non-technical audiences.
• Proactively improve models, processes, and analytical assets.
• Structure ambiguous problems into clear analytical approaches.