Analytics Engineer Placement

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, focusing 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 BI 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.

• 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.

Demonstrates expertise in building and maintaining end-to-end data pipelines, with a strong focus on data modeling, transformation, and delivery. Proficient in SQL and dbt, with a commitment to data quality and best practices in documentation and version control.