Customer Data Analyst

Key Responsibilities

• Analytics & Visualization (~55%)
• Build dashboards and visualizations for customers, making complex grid data clear and actionable.
• Translate customer business questions into analytical tasks, and communicate findings clearly to both technical and non-technical audiences.
• Deliver ad hoc and bespoke analysis to answer questions and surface insights that aren't yet available through the product.
• Validate outputs against quality standards to ensure analysis is accurate and trustworthy.
• Customer Delivery Support (~25%)
• Work alongside Power Engineers, who provide subject matter expertise and lead technical delivery, to support end-to-end customer engagements.
• Partner with Customer Data Engineers to ensure the data required for analytics is available, correctly mapped, and fit for purpose.
• Debug data and analysis issues with composure — triaging problems, communicating clearly with customers, and driving resolution.
• Product Support (~20%)
• Identify and communicate patterns across customer engagements to inform product direction.
• Partner with Product to translate bespoke analyses into repeatable, platform-ready features where there's enough demand and signal from customers across the market.
• Contribute to internal documentation, playbooks, and tooling that improve the efficiency and consistency of future analytics and delivery work.
• Bring a tool-first mindset: spot manual steps that can be automated, and take initiative to improve them.

Qualifications

• 2+ years of professional experience in data analytics, analytics engineering, data engineering, technical consulting, or a similar role combining hands-on data work with stakeholder engagement.
• Strong SQL skills and demonstrated ability to analyse, validate, and wrangle complex or messy datasets.
• Experience building dashboards and data visualizations.
• Proficiency in Python for data analysis, transformation, and scripting.
• Ability to communicate data-driven findings clearly to both technical and non-technical audiences.
• Experience with cloud data infrastructure and modern development practices (Git, command-line environments, collaborative codebases).
• Exposure to dbt or similar ELT/ETL frameworks.
• Strong problem-solving skills and a methodical approach to troubleshooting technical issues.
• Prioritization of proven problem-solving abilities and a self-starter mentality, comfortable working in a fast-paced startup environment.