Top Data & Analytics Jobs in Indianapolis, IN

The Manufacturing Analytics Engineer will manage the analytics pipeline, transforming operational data from OT systems into actionable insights for decision-making, while collaborating with Engineering, IT, and Operations.

Hands-on technical lead designing and delivering secure, scalable Azure analytics solutions. Lead solution architecture, guide implementation, mentor engineers, advise on Power BI architecture and governance, and support delivery, consulting, and reuseable asset creation.

The Senior Data Analyst will support marketing and sales, drive growth through analytics, and collaborate with key stakeholders to enhance data-informed decision making.

As an Analytics Manager, you'll build dashboards, provide strategic insights, and create a standard data language across teams to enable data-informed decisions.

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.

The Director of Analytics will lead the analytics strategy and operations for a top-grossing mobile app, driving data-driven decision-making and insights.

The Senior Product Data Analyst will analyze SaaS product usage, develop metrics, manage projects, and provide insights that support strategic decisions across teams.

The Senior Data Analyst will manage data, deliver impactful reporting, and provide actionable insights across the organization to optimize performance and drive growth.

Lead analytics engineering: architect, build, and maintain scalable data pipelines and dbt models; ensure data is research-ready; partner with Engineering, Data Science, and Commercial teams; implement product analytics and optimize data workflows for scale.