Top Data & Analytics Jobs in Chicago, IL

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.

Leads a client-facing healthcare analytics team that designs and maintains prior authorization decision automation programs. Consults PaaS clients, develops strategic roadmaps, translates requirements into technical blueprints, analyzes authorization and claims data, and builds KPI reports using visualization tools. Manages direct reports, improves analytics processes and AI tooling, partners with product and engineering teams, and ensures automation outcomes and client KPIs are achieved.

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.

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.