Data Analyst

About the role

We're looking for an Analytics Engineer / Data Analyst to unlock the value of our data assets by transforming and presenting data in a way that drives action. You’ll be a key voice in turning data signals into business decisions.

Responsibilities

• Data Modeling & Transformation - Design, build, and maintain dbt models that transform raw source data into clean, well-documented, analytics-ready tables in Snowflake.
• Establish and enforce naming conventions, testing standards, and documentation practices across the dbt project.
• Own the semantic layer — ensuring consistent metric definitions that all stakeholders can trust.
• Build and maintain executive and department-level dashboards that communicate performance clearly and without ambiguity.
• Partner with stakeholders across pricing/actuarial, sales, business development, and operations to understand reporting needs and translate them into durable, self-serve solutions.
• Distinguish between dashboards that inform decisions and dashboards that create noise — and build accordingly.
• Conduct deep-dive analyses to explain anomalies, validate hypotheses, and uncover signals in messy data.
• Synthesize findings into clear, concise narratives — written, visual, and verbal — appropriate for technical and non-technical audiences.
• Proactively identify inflection points in the data and connect them to operational or market causes.
• Contribute to strategic decisions by framing tradeoffs with data, not just describing what happened.
• Data Quality & Governance - Instrument data quality checks and alerting so issues surface before they reach decision-makers.
• Maintain data dictionaries and lineage documentation that make the platform legible to the broader organization.
• Partner with engineering to ensure upstream source data lands in a state that’s trustworthy and usable.

Requirements

• 3–5+ years of experience in analytics engineering, data analysis, or a hybrid role.
• Strong SQL — you write queries from scratch, optimize them, and know when a query is telling you something wrong.
• Hands-on experience with dbt (Core or Cloud) — model structure, ref/source, tests, documentation, incremental strategies.
• Proficiency with Snowflake or a comparable cloud data warehouse.
• Experience building dashboards in Metabase, Looker, Mode, Tableau, or similar.
• Proven ability to go from a vague business question to a structured analysis to a clear recommendation.
• Strong written communication — your documentation and stakeholder write-ups are as clear as your SQL.
• Plus experience in insurance, fintech, or real estate data environments.
• Familiarity with property data, underwriting data, or policy/claims datasets.
• Python for data analysis (pandas, notebooks, scripting).
• Exposure to data modeling patterns — star schema, slowly changing dimensions, wide tables — and when to use which.
• Experience working in a startup or early-stage data team where you had to build the foundation, not just extend it.