Top Data Management Jobs in Indianapolis, IN

Lead the data management strategy and team to design, build, and maintain scalable ETL pipelines and data infrastructure. Implement data governance, ensure data quality, optimize performance using tools like Airflow, Iceberg, Trino, and Spark, and collaborate with cross-functional teams to make data accessible and actionable.

Lead data initiatives, develop machine learning models, conduct analysis to improve programs, and collaborate with cross-functional teams to drive results for health insurance enrollment.

The Clinical Data Manager oversees clinical trial databases, creates documentation, reviews data, trains staff, and ensures compliance with regulatory standards.

The Senior Clinical Data Manager oversees clinical trial databases, mentors junior staff, and ensures data quality and compliance with regulatory standards.

Manage and optimize GTM tech stack, oversee Salesforce CRM, ensure data quality across systems, build data models, and create actionable insights for go-to-market strategy.

Lead a team of Data Scientists, Analytics Engineers, and Data Engineers to drive messaging strategies through analytics and data science, ensuring collaboration across various business domains.

The Manager leads the Predictive Analytics and Data Science team, develops predictive models, and collaborates with cross-functional teams to drive data-driven solutions in healthcare.

The Senior Data Manager will design, configure, and launch clinical trials on Citrus while managing data quality and collaborating with product teams and stakeholders.

As a Data Manager, you will oversee data management for clinical trials, ensuring quality outcomes, designing data capture tools, and collaborating on product development.

As a Data Analytics Manager, you will lead a team to analyze data, drive strategic insights, manage projects, and optimize mobile app performance for top brands.

As a Curriculum Manager, you will manage the content development lifecycle, collaborate with instructors, and improve data science curriculum, ensuring technical accuracy and pedagogical effectiveness.