Top Data & Analytics Jobs in Indianapolis, IN

Analyzes Recidiviz interventions using Python and SQL, builds statistical models, measures historical and projected impact, develops automated metrics workflows, and communicates findings to stakeholders. The role defines product usage and impact measures, supports experimental and quasi-experimental analyses, and informs product strategy and fundraising partnerships focused on criminal justice outcomes.

Build, maintain, and optimize data warehouses and ETL/ELT pipelines supporting ticketing, sales, marketing, fan engagement, and analytics. Integrate NFL and third-party data into Snowflake and Azure, develop modeled datasets, monitor data quality and ingestion, troubleshoot pipeline issues, and document schemas and workflows. The role also supports privacy, consent, data governance, and cybersecurity controls while collaborating with technical and business stakeholders. This is a full-time, onsite position in Indianapolis.

Leads PBM data integration delivery across claims, eligibility, accumulator, and encounter systems. Profiles and validates data, writes advanced SQL, resolves reconciliation discrepancies, investigates production issues, and partners with engineering on scalable healthcare integration architecture. Owns timelines, risks, vendor dependencies, testing, UAT, migration, rollback planning, executive reporting, and post-deployment support. Requires deep PBM claims-processing expertise, data architecture experience, and hands-on knowledge of ETL, secure file transfer, job scheduling, and healthcare data standards.

Designs, develops, tests, troubleshoots, and deploys cloud-based data and analytics solutions using AWS, infrastructure as code, and software development practices. The role involves data integration, automation, application and data model design, system integration, code reviews, testing, troubleshooting, and stakeholder collaboration. It also provides informal leadership through mentoring, design sessions, coding sessions, and communication of complex technical concepts.

Designs, builds, and maintains scalable AWS and on-premises data pipelines supporting genomic data science models. Responsibilities include API ingestion, storage, transformation, data modeling, workflow development, troubleshooting, documentation, infrastructure-as-code, and cross-functional collaboration. The role requires hands-on AWS pipeline development, HPC experience, container orchestration, Python and SQL expertise, CI/CD, secure access management, and production data engineering leadership in a consulting environment.

Leads enterprise information architecture, business intelligence, and data warehouse strategy and delivery. Oversees BI and data warehouse teams, establishes roadmaps and governance, mentors technical staff, manages project priorities, and ensures high-quality implementation. Designs enterprise data models, datamarts, metadata, ETL processes, reporting strategies, and integration artifacts. Partners with business and technology stakeholders to align data solutions with strategic goals, improve adoption, track value, and drive continuous improvement.

Supports strategic initiatives and complex pricing projects by analyzing business data, developing performance metrics, preparing reports, and recommending pricing strategies. Coordinates cross-functional project teams and presents findings to Finance, Supply Chain, Sales, Logistics, and Marketing. Also assists with team training, mentoring, report review, and accuracy oversight.

The Business Analyst elicits, analyzes, documents, and validates business, technical, reporting, and data requirements for healthcare and social services programs. Responsibilities include requirements workshops, process and data documentation, Medicaid data analysis, data quality triage, user acceptance testing, stakeholder coordination, training, demos, and solution delivery support.

Assess legacy on-premises infrastructure and design a cloud-native data architecture for AccuWeather For Business. Create current- and target-state designs, modernization roadmaps, sequencing plans, technical specifications, and risk mitigations. Partner with engineering and data teams to implement scalable, secure, resilient backend data systems while maintaining reliable enterprise data delivery. Contribute hands-on code, data models, schemas, capacity estimates, cost analysis, and architecture decisions.

Lead and grow a data engineering team to build a trusted, fast, composable, and self-serve data platform. Define architecture and roadmap for ingestion, transformation, governance, and analytics; ensure reliability, observability, and security; partner cross-functionally with Product, Analytics, Security, and Engineering; be a hands-on player-coach delivering scalable data systems using Snowflake, dbt, and self-serve analytics.