Data Analyst - Business Insights

Role and Responsibilities

• KPI Definition & Governance: Work with stakeholders to turn business questions into precise metric definitions - establishing the grain, time basis, and exclusion rules, and documenting each one. Maintain the definitions as the business changes, so that "Billed Revenue" or "Overdue Task" means one thing across the company rather than four.
• Operational & Strategic Reporting: Build for both audiences and understand why they differ. The same underlying data serves both, but the grain, refresh cadence, and framing are not interchangeable.
• Business Partnership: Partner with the teams you support and learn how they operate - what they are accountable for, what decisions they make, and where they are currently flying blind. Push back when a requested metric is a vanity number, cannot be acted on, or would incentivize the wrong behavior.
• Reporting Ownership: Own the end-to-end reporting layer for the business units you support. Become the person those teams come to, and the person who can explain why a number looks the way it does.
• SQL Development & Data Lineage: Write and optimize queries against our data warehouse and maintain clear traceability from every figure on a dashboard back to its source system. When a number is challenged, you should be able to walk it back to the row it came from.
• Dashboard Maintenance & Development: Keep existing views accurate, performant, and resilient to upstream changes. Build new pages and views in our internal React application, following established patterns - writing the query, wiring the API layer, and implementing the front-end view.
• AI-Augmented Development: Use AI coding tools as a primary part of your workflow, directing them to generate and refactor code while applying your own judgment to review, test, and validate every output before it's pushed to production.
• Ad Hoc Analysis & Communication: Answer one-off business questions under pressure, present findings to non-technical partners with the caveats intact, and recognize when a recurring request should graduate into a permanent dashboard.

Desired Qualifications

• Bachelor's degree in Economics, Finance, Data Analytics, Data Science or Computer Science.
• 2+ years' experience in data analysis, data visualization, generating financial statements or dashboard creation.
• Working SQL proficiency. Comfortable with joins, aggregations, and grouping, and able to read a query someone else wrote and determine what it does. Window functions are a plus, not a prerequisite.
• Demonstrated care with numerical accuracy. When a result surprises you, your instinct should be to verify it rather than publish it.
• Strong written communication. Much of this role is explaining what a number means, and what it does not mean, to someone who does not work with data.
• Comfort reading and reasoning about code you did not write. You do not need to be a strong programmer - you do need to open an unfamiliar file, work out what it is doing, and make a careful change.
• Openness to AI-assisted development tools (Claude Code, Grok Build, Copilot, or similar) as a core part of your daily workflow, paired with the discipline to review their output rather than trust it.
• An audience-first instinct. Before building anything, you want to know who will look at this, what they will do differently as a result, and how they will know if the number is wrong.
• Genuine curiosity about how a business operates - revenue, cost, churn, utilization, service levels - and interest in learning the operational mechanics of the teams you support.

Nice to Have

• Any experience defining or maintaining a metric that other people relied on - in a job, an internship, a student organization, or a personal project.
• Familiarity with common analytical constructs and their pitfalls: rates and ratios, cohort analysis, period-over-period comparison, mix shift, survivorship.
• Experience of writing software in any languages (Python, JavaScript/TypeScript, R, C#, Java)
• Exposure to React, TypeScript, or any modern front-end framework.
• Experience with a columnar or cloud data warehouse (StarRocks, Snowflake, BigQuery, Redshift, ClickHouse).
• Data engineering or data pipeline experience (ETL, Dagster, Apache Airflow, Apache Flink, Kafka, or similar).
• Familiarity with git, dbt, or any data orchestration or lineage tooling.
• Background in an operations, finance, or customer-facing role.

Salary

This position has a base compensation package offered at $95,000 commensurate with education and background experience.

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