Data Analyst

SafeLease•Austin, TX

1d•Hybrid

About The Position

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.

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

Nice To Haves

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

Responsibilities

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
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