Analytics Engineer, FiRE

Rightway•New York, NY

About The Position

We’re hiring an Analytics Engineer to help design and build a PBM Financial Risk Engine that brings together claims, contract terms, pricing and utilization data to support financial forecasting, risk assessment and performance monitoring. This role is ideal for someone who is strong in dbt and analytics engineering and who enjoys working at the intersection of healthcare economics, PBM operations and data modeling. The ideal candidate will play a pivotal role in developing and maintaining our financial risk engine data models, onboarding new data sources and optimizing existing data sources and packaging the data in a way that promotes seamless reporting while paving a way for innovation through advanced analytics. This role will involve delivering and analyzing PBM financial data for various analytics use cases within our Unified Data Warehouse (UDW) and productionalizing AI and ML solutions through the curated data to drive impactful healthcare insights.

Requirements

Expert level proficiency in SQL and statistical programming (Python, R).
3 to 4 years working experience with data modeling, dbt and analytics engineering best practices (testing, documentation, incremental models, semantic layers).
Experience in cloud platforms (preferably in AWS) and hands-on experience in cloud data warehouses (Redshift or Snowflake).
Business acumen with exposure to PBM, healthcare finance, underwriting or actuarial concepts.
Experience modeling large scale claims data and translating contractual or financial rules into clear, testable data logic.
Strong analytical and problem-solving skills.
Ability to understand, tackle, and solve problems from both technical and business perspectives.
Comfortable partnering closely with finance, underwriting and operations stakeholders to align data models with real world financial decisions.
High attention to detail and a bias toward accuracy, transparency and explainability in financial reporting.

Nice To Haves

Experience operationalizing ML models is a plus.

Responsibilities

Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems using PBM data.
Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM financial risk engine, leveraging claims, eligibility, pricing, rebates, guarantees and contract data in our Unified Data Warehouse (UDW).
Translate complex PBM contract and pricing structures (e.g plan paid vs. member paid, rebates, guarantees, caps, fees, exclusions) into transparent, auditable data models that enable financial analysis and risk assessment.
Partner closely with underwriting, finance, client success and PBM operations teams to understand financial assumptions, risk drivers and reporting requirements and convert them into reliable metrics and models.
Build curated financial and risk data marts and semantic layers that enable self service analysis for forecasting, scenario modeling and executive reporting among other things.
Own core financial KPIs and risk metrics E2E from raw claims and contract inputs through production grade models, ensuring consistency and traceability.
Implement automated data quality checks, reconciliations and controls to validate financial outputs and ensure alignment with source systems and contractual logic.
Continuously optimize data models and warehouse performance to support large scale claims volumes and time-sensitive financial analysis.
Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability.
Communicate complex financial insights clearly through data memos, documentation and presentations for both technical and non technical stakeholders.
Explore advanced analytics and predictive modeling use cases (e.g utilization forecasting, trend analysis, risk stratification, margin sensitivity) to enhance financial planning and decision making. Experience operationalizing ML models is a plus.