Data Analyst

About Halcyon Labs At Halcyon Labs , we're rethinking how P&C insurance is sold in an age of technological change. We believe the industry's biggest inefficiencies aren't technical problems — they're structural ones. And we're building the team to tackle them. Halcyon Labs is a profitable insurance business that designs, underwrites, and distributes specialty coverage for commercial property owners and their tenants. Most insurance companies either distribute products or bear the risk — we do both. We back our policies with our own capital, which means we control the full stack: product design, tech, and the speed at which we move. That end-to-end ownership lets us offer customers real flexibility, saving time and money for more than 4,000 properties insured for billions in value nationwide. We're a team of 70, growing over 100% annually, and we've done it without sacrificing profitability or culture. Here, you'll get high discretion and a wide aperture of problems to solve. We embrace the newest technologies, move fast together, and operate with the intensity of a small company where every person's work is visible. If you're looking for a place to sharpen your craft alongside people who take their work seriously, you'll fit right in. 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. This isn't a "pull reports on request" role. You'll build the infrastructure that makes reliable reporting possible, go deep on unexplained patterns, and proactively surface insights that change how the business operates. You'll work closely with pricing, sales ops, product, and leadership — translating between technical data realities and business questions that don't always arrive in clean form. What You'll Do 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 Reporting & Dashboards 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 Analysis & Insight 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 Required 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 Strong 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 What Success Looks Like In your first 90 days, you've learned the data landscape, identified the highest-leverage gaps in our current modeling and reporting, and shipped your first set of dbt models and dashboards into production. Stakeholders know who to come to with data questions — and the answers they get are accurate and on time. At six months, you've meaningfully impr