Analytics Engineer, Growth & Placement

Acrisure•Austin, TX

7d•Hybrid

About The Position

Acrisure is investing significantly in its data infrastructure, most visibly through the adoption of Palantir Foundry as our enterprise analytics and application platform. The Growth & Placement Analytics team supporting the executive leadership of our North America Insurance business sits at the forefront of that buildout, producing the data assets that drive strategic decisions across the organization. As an Analytics Engineer on the Growth & Placement team, you will design, build, and maintain the pipelines and data models that make the team's analytical work possible. You will work closely with senior engineers and analysts on the team to translate strategic and operational questions into reliable, well-modeled data assets — extending dbt models on BigQuery, building Palantir Foundry Ontology objects and workflows, and standing up new pipelines end-to-end. Palantir Foundry experience is a differentiator but is not required. We are pragmatic about where we set the bar. What matters most is that you have a strong foundation in data modeling and a genuine willingness to become expert in Palantir's ecosystem as part of your growth at Acrisure. If you know dbt and BigQuery or another data warehouse well, we can teach you the rest. This is a growth-stage role on a small, high-visibility team that is still defining how it operates. You will have meaningful influence over how the data layer takes shape, and your work will directly enable insights that reach senior executive stakeholders, including members of the C-suite.

Requirements

4–6 years of professional experience in analytics engineering, data engineering, or a closely related data infrastructure role.
Strong SQL skills across a modern cloud data warehouse.
Hands-on experience with dbt or an equivalent transformation framework — you understand the mechanics of models, tests, documentation, and incremental materialization strategies.
Demonstrated ability to build data pipelines from scratch, including sourcing, transformation logic, and delivery layer.
A genuine interest in learning Palantir Foundry and the intellectual openness to develop deep expertise as part of your growth on the team.
Comfort operating in ambiguity within a small, fast-moving team — you can manage your own priorities and communicate when support is needed.
A business-aligned mindset: you care about why a pipeline exists, not just whether it runs.
A skeptic's relationship with AI tools — you use them to move faster, but you validate outputs and take responsibility for the code you ship.
A bachelor's degree in computer science, Mathematics, Statistics, or a related field, or equivalent professional experience.

Nice To Haves

Direct experience with Palantir Foundry — even tangential exposure is valued.
Python proficiency, particularly for data transformation, pipeline orchestration, or scripting.
Familiarity with Power BI, Tableau, or other BI platforms used for delivery to business stakeholders.
Experience in insurance, financial services, or other data-intensive B2B industries.
Exposure to agile development practices, Git-based workflows, and project tracking tools like Jira.

Responsibilities

Design, build, and maintain end-to-end data pipelines — from raw source ingestion through transformation, modeling, and delivery — with reliability and performance in mind.
Develop and extend dbt models on top of Acrisure's data warehouse, with particular focus on the operational and SSOT data layers that underpin enterprise reporting.
Build Palantir Foundry applications, Ontology objects, and workflows that surface data to analysts and business stakeholders in forms they can act on moving the team beyond ad hoc querying toward scalable, reusable tooling.
Partner closely with analysts and business intelligence teammates to understand downstream needs and engineer upstream data assets accordingly.
Uphold data modeling best practices in the work you touch: clear documentation, version control via Git, incremental build strategies, and data quality testing.
Leverage AI tools (Claude, Palantir AI FDE, Gemini, and others) to accelerate development, while applying rigorous critical evaluation to any AI-generated code or logic before it ships.
Contribute to the pod's overall data strategy, surface infrastructure gaps, raise architectural improvements, and help shape how the Growth & Placement pod's data layer evolves.