Top Remote Data & Analytics Jobs in Indianapolis, IN

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Provide ad-hoc data and reporting support to Sales and internal teams using SQL, Databricks/Count, and spreadsheets. Clarify requirements, investigate discrepancies, resolve basic data issues, support routine data operations, and create reusable queries/reports and documentation to enable self-service.

Lead SAP Data Migration activities for enterprise transformation projects, ensuring data quality, governance, and compliance while collaborating with cross-functional teams.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

Design, analyze, and document data products and technical specifications. Perform large-scale data profiling and SQL analysis, support data governance, partner with stakeholders to remediate issues, and help implement data quality, lineage, and ingestion standards.

This role involves writing user stories, supporting agile team processes, and driving customer discovery to enhance technology solutions for clients.

The Actuarial Specialist produces analyses, manages projects, and oversees team workload to enhance actuarial processes in the organization, ensuring business growth and profitability.

The Data & Analytics Architect will design and implement analytics solutions, support client engagements, and lead data architecture projects, requiring extensive technical and consulting skills.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.