Top Data & Analytics Jobs in Indianapolis, IN

Leads integration of healthcare payer feeds and internal enterprise data into WellBe’s data platforms. Responsibilities include data mapping, transformation, validation, normalization, quality monitoring, governance, metadata and lineage management, requirements gathering, and supporting ingestion pipeline delivery. The role partners with engineering and analytics teams, serves as a subject matter expert, and ensures reliable data for value-based care, reporting, and operational use cases.

Leads the architecture, implementation, and evolution of enterprise data platforms, including Master Data Management, data lakes, lakehouses, and cloud data services. Manages data engineers and developers, establishes engineering standards, oversees Azure-based integrations and pipelines, and owns data applications, databases, security, governance, and production support. The role also directs technical strategy, manages multiple initiatives, and communicates architecture and delivery plans to stakeholders.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

Analyze insurance product and portfolio performance, build dashboards and reporting tools, validate data, and translate complex insurance questions into actionable recommendations. The role supports rate revisions, state expansion, product development, and profitable growth while partnering with Product, Engineering, Data, Underwriting, Compliance, and other teams. It also uses SQL, business intelligence platforms, and AI tools such as Claude to improve workflows and decision-making.

Leads digital performance measurement, analytics, reporting, conversion optimization, experimentation, attribution, and customer journey analysis across global digital platforms. Builds executive dashboards, identifies experience and funnel improvements, establishes KPI and governance frameworks, and translates complex data into business recommendations. Partners with Marketing, Product, UX, Technology, and Analytics teams while managing and mentoring Marketing Analysts.

Leads qualitative and quantitative market research to uncover customer, market, competitive, and healthcare insights. Designs and manages studies, analyzes data using statistical and AI-assisted tools, develops reports and dashboards, presents recommendations, maintains research quality, and coordinates external partners. Collaborates with product, engineering, sales, marketing, and customer success teams to influence product strategy, growth, innovation, and customer experience initiatives.

The Data Governance Specialist develops and enforces data governance, quality, privacy, risk, and compliance standards across organizational data assets. Responsibilities include managing metadata, data dictionaries, lineage, classification, retention, PII controls, DPIAs, risk assessments, audits, dashboards, and data-quality remediation. The role partners with engineering, security, legal, and business teams, provides stakeholder training, investigates data integrity issues, supports incident escalation, and automates governance processes through scripting.

Lead the data engineering strategy and architecture for distributed pharmaceutical data systems. Design and scale pipelines, APIs, and data platforms while ensuring reliability, governance, quality, and PII/PHI protection. Drive technical decisions, improve developer efficiency, deliver business impact, influence cross-functional roadmaps, mentor engineers, communicate with executives, and promote organizational adoption of new technologies and processes.

Lead product analytics for Guest or Host App: analyze large datasets, design and evaluate experiments, build metrics and reporting tools, apply statistical and causal methods, partner cross-functionally, present insights to stakeholders, and leverage AI to automate tasks.