Data Analyst

Key Responsibilities:

• Own analytical workstreams end-to-end — synthesize input from users, business stakeholders, and D&T leadership into a clear, prioritized set of questions to answer and metrics to track.
• Build, maintain, and refine dashboards, reports, and self-service analytics: write clean, well-documented SQL, groom data backlogs, lead refinement sessions with stakeholders, and actively participate in scrum ceremonies, demos, and lead user testing and rollout of analytical products.
• Drive data discovery alongside Product and UX partners — conduct user research on analytical needs, run feedback sessions, and champion the end-user perspective in every metric and visualization decision.
• Translate business questions into execution by collaborating closely with Engineering and Data Engineering, turning analytical requirements into well-modeled, reliable, queryable data.
• Define and deliver specific analyses end-to-end, from problem framing through validation, presentation, and measurement of impact.
• Represent your analysis to business leadership and D&T leadership — providing crisp, regular readouts and connecting day-to-day work to broader strategic goals.
• Spot data quality problems before they become blockers: proactively investigate anomalies, resolve issues, and communicate with clarity and urgency when it matters.
• Model Client RISE values on your team and in every cross-functional interaction — lead by example, build trust fast, and bring people along.

Key Requirements and Technology Experience: 

• 3–5+ years of experience in Data Analytics, Business Intelligence, Business Analytics, or a related quantitative field — with a track record of delivering analyses that influenced decisions.
• A bachelor’s degree in a quantitative discipline is preferred; relevant experience counts.
• Strong SQL skills with hands-on experience querying large, complex datasets, plus fluency building dashboards and reports in modern BI tools (Power BI, Tableau, Looker, or similar). Proficiency in Python or R for analysis and automation is a plus.
• Strong instincts for prioritization — you know which questions matter, and you know how to explain why.
• The communication skills to work across levels: from a debugging session with a data engineer to a C-suite readout, you adjust your style and land your message.
• A bias for the truth — you ask hard questions of the data, validate your assumptions, and let what you find change your mind.
• Comfort navigating ambiguity, managing competing priorities, and keeping work focused when requirements shift.
• Proficiency with Microsoft Office tools (Word, Excel, Outlook, PowerPoint) and familiarity with modern analytics and data warehousing platforms (e.g., Snowflake, Databricks).
• Experience with data modeling, data quality, and data governance — you understand data and how it interacts with products and decisions.
• Experience in Commercial Real Estate (nice to have).