Top Data & Analytics Jobs

Lead a healthcare analytics and clinical configuration team, owning end-to-end auto-decisioning strategies for new client implementations. Translate business needs into scalable analytical frameworks, develop performance tracking and optimization initiatives, and deliver actionable insights from healthcare data. Partner with data governance and cross-functional teams to ensure explainable, responsible analytics. Manage team hiring, mentoring, coaching, and day-to-day operations while influencing product and business outcomes.

Own actuarial models and client-facing reporting measuring cost savings, outcomes, and performance of healthcare prior authorization programs. Analyze authorization and claims data, conduct impact evaluations, reconcile projected and realized results, investigate trends, and present executive-level insights to customers and leadership. Partner with clinical, customer success, data, and actuarial teams while maintaining reproducible, auditable methodology and documentation.

Develop and maintain financial and cost-savings models for prior authorization initiatives. Analyze healthcare claims, authorization trends, decision patterns, and cost drivers; quantify projected savings; and deliver executive reports and recommendations. Partner with clinical and customer success teams to address authorization challenges, conduct post-implementation assessments, and refine savings strategies. This remote-first role requires approximately 5% travel for onboarding and occasional meetings.

Leads healthcare analytics projects using clinical, claims, pharmacy, and eligibility data to evaluate cost, utilization, quality, and program performance. Develops statistical analyses, predictive models, dashboards, and reporting solutions; identifies optimization opportunities; partners with clinical, product, engineering, and business teams; presents recommendations to stakeholders; and mentors junior analysts.

Analyze healthcare claims, authorization, utilization, cost, operational, and clinical outcomes data to optimize auto-decisioning strategies and client implementations. Build reporting solutions, dashboards, visualizations, and self-service analytics using SQL and Tableau or Power BI. Identify performance improvements, track industry trends, communicate actionable insights, and collaborate with clients and cross-functional teams on rule refinement and metric tracking.

Designs and delivers scalable cloud-native data solutions using AWS, Apache Iceberg, dbt, Python, SQL, and Spark. Builds batch and near-real-time pipelines, ingestion and transformation frameworks, data quality controls, observability practices, and legacy modernization solutions. Supports schema evolution, partitioning, compaction, query optimization, architecture discussions, code reviews, and mentoring. Also contributes to information security management, risk assessments, continuity testing, security policies, and ISMS reporting.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

The role involves managing a team of data analysts to ensure the quality and usability of healthcare market datasets, implementing analytical standards, and translating complex data outputs into actionable business insights.

Lead design and implementation of scalable ETL/ELT pipelines, ensure data quality and governance, build dashboards and automated reporting, mentor engineers, troubleshoot production incidents, and improve platform reliability, performance, and observability to support analytics and business KPIs.