Top Analysis Reporting Jobs in Indianapolis, IN

Analyze performance marketing data, including traffic quality, unit economics, reporting, attribution, A/B testing, forecasting, and monetization. Build dashboards, anomaly alerts, and data models using BigQuery, dbt, and Tableau. Partner strategically with performance teams to identify growth opportunities, improve channel evaluation, and guide scaling decisions through SQL, Python, and statistical analysis.

Analyze marketing performance across paid media, CRM, email, and customer engagement channels. Prepare recurring reports, consolidate data into actionable insights, support client recommendations and optimization strategies, and perform quality checks on reports and datasets. Collaborate with strategy, client services, media, and analytics teams while using AI tools responsibly to improve reporting efficiency.

Analyze complex financial and business data to guide planning, forecasting, investment decisions, and strategic growth. Partner with FP&A, Accounting, RevOps, Product, Marketing, Data Engineering, and analytics teams; automate recurring workflows; build self-service dashboards and data assets; improve analytics infrastructure; and communicate actionable insights to technical and non-technical stakeholders.

Investigate offline and direct mail channel performance, attribution, vendor data, and measurement methodologies. Build response, lift, allocation, and regression models; manage direct mail testing data pulls and mail merges; create KPI dashboards; and communicate actionable recommendations to marketing stakeholders. The role requires proactively identifying opportunities, validating data and vendor claims, and translating complex analytical findings into business decisions.

Design and lead scalable data engineering solutions across Databricks and Microsoft Azure. Define enterprise data modeling standards, architect pipelines and integrations, establish engineering best practices, troubleshoot complex platform issues, and guide modernization and cloud migration initiatives. Provide technical leadership and mentorship while ensuring data platforms meet security, quality, governance, reliability, performance, and maintainability standards.

Lead advanced product analytics for a consumer product team by analyzing complex data, designing experiments, applying statistical and causal inference methods, developing metrics and reporting tools, and delivering actionable recommendations to cross-functional stakeholders and senior leadership. The role requires strong Python or R and SQL skills, product analytics experience, AI-assisted analytics proficiency, and the ability to influence product strategy.

Develop and maintain automated data pipelines, API-driven workflows, and data integrations using Python, SQL, Databricks, and data lake architectures. Improve data quality through validation, schema design, and provider data mapping across diverse sources. Build analytics-ready datasets for predictive analysis, Power BI reporting, and GIS-based access analyses. Document data flows and technical configurations, support knowledge transfer, and collaborate with analysts, product owners, contractors, and government stakeholders.

Builds and maintains scalable data pipelines, ETL infrastructure, data warehouses, and analytics tools. The role assembles complex datasets, automates processes, optimizes data delivery architecture, and supports cross-functional data initiatives. Collaborates with executive, product, data, and design stakeholders to resolve technical issues and improve analytics systems. Requires proficiency in SAS, Python, Java, MATLAB, SQL, NoSQL, Hadoop, MapReduce, Hive, Presto, UNIX, and Linux, with JavaScript, R, and basic machine learning preferred.

Leads data analytics and operational transformation for the Magnetic Resonance team. Oversees exploratory analysis, predictive modeling, forecasting frameworks, metrics, visualizations, and scenario-based insights to improve delivery and installation performance. Partners with regional, segment, business, and technical stakeholders to support planning, strategic initiatives, data transparency, and continuous improvement. Travel to manufacturing or customer locations may be required.

Lead enterprise metadata strategy, standards, governance, capture, curation, validation, lineage, and lifecycle management across data products and business assets. Partner with data governance, engineering, quality, analytics, modeling, and stewardship teams to improve data trust, discoverability, reuse, and AI readiness. Drive metadata automation, catalog synchronization, certification support, issue remediation, and stakeholder communications across a complex enterprise data environment.