Senior Strategic Data Analyst, Growth & Placement

Acrisure•Austin, TX

6d•Hybrid

About The Position

Acrisure's Growth & Placement Analytics team supporting the executive leadership of our North America Insurance business operates at the intersection of data and business strategy, translating complex data assets into decisions that drive revenue and operational performance. As a Senior Strategic Data Analyst on the Growth & Placement pod, you will operate as a trusted senior individual contributor owning strategically ambiguous, high-stakes analytical questions facing NAI leadership and driving them from initial framing through executive-ready recommendation. This is not a role where the questions are handed to you. You will independently identify what the business needs to understand, structure the analytical approach, and shape the agenda in partnership with senior executives. Your findings will directly inform decisions with enterprise-wide impact, and the analytical products you build will be used across NAI leadership, operating teams, and downstream analytical functions. Beyond delivery, you will set the standard for analytical rigor within the team. This is a thought-leadership role: you establish the patterns others follow — how we frame ambiguous problems, how we communicate data limitations, how we integrate AI tooling into production analytical workflows without compromising trust. You are expected to raise the ceiling of what the team can do. Acrisure is investing significantly in its data infrastructure, most visibly through the adoption of Palantir Foundry as our enterprise analytics and application platform. You will have the autonomy and expectation to shape how those investments translate into analytical value for the business, and to architect the data assets required to answer questions that don't yet have precedent.

Requirements

5–8 years of professional experience in data analytics, analytics engineering, business intelligence, or a strategy-adjacent analytics role, with a clear trajectory of increasing scope and autonomy over that time.
Expert command of SQL: you write complex analytical queries fluently across window functions, CTEs, and multi-table joins; you optimize your own queries; and you can architect data logic that others build on.
A demonstrable track record of owning end-to-end analytical products that serve broad stakeholder populations — from metric definition and data modeling through self-service delivery, not just contributing to work owned by others.
Deep experience with a modern cloud data warehouse and hands-on data modeling experience with dbt or an equivalent transformation framework.
Proven ability to independently frame ambiguous business problems, structure the analytical approach, and drive multi-quarter initiatives with minimal oversight.
Executive-grade communication — you can walk into a C-suite conversation, set the terms of the discussion, and defend your analytical position in real time. Written and verbal fluency at that altitude are both expected.
The intellectual honesty and confidence to say 'the data doesn't fully answer this question, and here's why' — and to make that observation productive rather than deflating.
Experience integrating AI or ML into analytical workflows at production quality — for example, Gen AI classification pipelines, prompt-engineered analytical automation, or Claude/LLM-integrated analytical products.
Experience with a BI or visualization tool (Tableau, Power BI) and a track record of delivering to multiple audiences from a single data foundation.
A genuine interest in developing deep expertise in Palantir Foundry and the intellectual openness to become one of the team's Foundry authorities.
A skeptic's relationship with AI tools — you use them, you benefit from them, but you validate their outputs rigorously before letting them inform business decisions or ship to production.
A bachelor's degree in a quantitative or analytical field, or equivalent professional experience. Advanced degrees in analytics, data science, or a related field are welcomed but not required.

Nice To Haves

Direct hands-on experience with Palantir Foundry, including Ontology modeling or Workshop application development.
Python proficiency, particularly for analytical automation, data transformation, or ML/AI pipeline development.
Experience in insurance, financial services, or other complex B2B commercial environments.
Prior experience directly presenting findings to C-suite audiences and defending analytical recommendations in executive forums.
Experience establishing analytical standards, playbooks, or reusable patterns adopted by a broader analytics team.
Exposure to agile development practices, Git-based workflows, and project tracking tools like Jira.

Responsibilities

Own the analytical framing of the most strategically important and ambiguous questions facing NAI leadership — deciding what merits investigation, defining the approach, and driving the work from problem definition through executive-ready recommendation with minimal oversight.
Design and deliver enterprise-scale analytical products end-to-end — from metric definition and data modeling through self-service delivery — that serve multiple audiences across NAI leadership, operating teams, and downstream analytical functions.
Independently engage senior executive stakeholders to shape the analytical agenda rather than react to it, and to translate ambiguous business questions into structured, defensible answers.
Lead multi-quarter analytical initiatives that require sustained investigation, integration of insights across functions, and pattern recognition across large, multi-source datasets.
Set the standard for analytical rigor across the team, establishing patterns for how we frame problems, communicate data limitations, and build analytical trust with executive audiences.
Write and optimize expert-level SQL against Palantir Foundry; architect the data models and pipelines required to support new categories of analysis; extend dbt models and Palantir Ontology objects where the analysis demands it.
Architect and productionize AI-augmented analytical workflows such as Generative AI classification pipelines and integrated analysis that scale analytical capacity while preserving rigorous verification standards. You define what "trustworthy" looks like when AI is in the workflow.
Champion intellectual honesty about what the data does and doesn't support — setting the tone for the team on when to say "we don't know yet" and how to make that observation actionable rather than paralyzing.

Benefits

Competitive compensation
Generous vacation policy, paid holidays, and paid sick time
Medical Insurance, Dental Insurance, and Vision Insurance (employee-paid)
Company-paid Short-Term and Long-Term Disability Insurance
Company-paid Group Life insurance
Company-paid Employee Assistance Program (EAP) and Calm App subscription
Employee-paid Pet Insurance and optional supplemental insurance coverage
Vested 401(k) with company match and financial wellness programs
Flexible Spending Account (FSA), Health Savings Account (HSA) and commuter benefits options
Paid maternity leave, paid paternity leave, and fertility benefits
Career growth and learning opportunities