Top Remote Data & Analytics Jobs in Indianapolis, IN

Lead the data organization to define data strategy, build scalable cloud-native pipelines, enable self-service analytics, drive AI/ML initiatives, and manage a multidisciplinary team to inform product, clinical, and business decisions.

As a Data Engineer at LiveKit, you will manage the analytics infrastructure, develop scalable GCP-based data pipelines, and ensure effective data movement and transformation processes, while collaborating with the Analytics and engineering teams.

Lead statistical programming for clinical trials, oversee programming deliverables, ensure compliance with CDISC standards, supervise staff, and develop programming solutions.

The Sr Data Enablement Specialist will enhance data governance, support BI tools, implement AI workflows, and provide training resources to improve data utilization across teams.

The Data Engineer Senior will optimize data pipelines, work within scrum teams, mentor others, and improve data quality, reliability, and governance.

The role involves supporting sales and services initiatives through data analysis, relationship management, and cross-functional collaboration to inform strategies and optimize processes.

The Senior Data Architect designs and implements data architecture solutions, leveraging modern platforms and methodologies to enhance data management, modeling, and integration across applications and stakeholders.

The IBM TM1 Developer will manage the SDLC for Finance applications, focusing on TM1 and migrating from Essbase, while collaborating with global teams.

Design, develop, and maintain enterprise Power BI dashboards and reports. Integrate data from SQL Server, Azure, Excel and other sources, perform data modeling and DAX calculations, validate data, and document data models and reporting logic while partnering with business stakeholders.

Design, build, and scale data pipelines and integrations for the TDX platform. Implement REST API integrations, ingest and unify customer data, optimize pipelines for performance and quality, ensure data security and governance, and collaborate with Product, Data Science, and customer teams to deliver reliable analytics-ready data.