Top Remote Analysis Reporting Jobs in Indianapolis, IN

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Create client-facing analyses and reusable data products for Product, Analytics, Client Success, and Sales teams. Analyze large healthcare datasets to identify behavioral patterns among medical professionals, automate code, and communicate insights to technical and non-technical stakeholders. The role combines SQL, Python-based exploratory data analysis, data visualization, stakeholder collaboration, and opportunities to grow into presentation-focused or advanced technical work.

Own customer data migration projects end-to-end: review, clean, map, import, validate, and QA records (events, tickets, customers, orders, donations, opt-ins, seating). Troubleshoot and monitor integrations (POS, CRM, marketing, reporting) using SQL, CSVs, APIs, webhooks, logs and admin tools. Build repeatable playbooks, documentation, and processes to reduce engineering dependency and ensure production data accuracy.

The SI Business Analyst will drive integration between business needs and technical delivery, focusing on insurance and healthcare systems. Responsibilities include capturing requirements, performing gap analysis, designing workflows, and leading cross-functional teams to optimize system performance and support digital transformation initiatives.

Lead marketplace analytics for Affirm’s two-sided ecosystem by optimizing monetization, supply, demand, pricing, profitability, and user experience. Build KPI dashboards, analyze marketplace and affiliate performance, conduct experiments, evaluate incentives and placement, and perform loan-level profitability analyses. Partner cross-functionally with Product, Engineering, Sales, Client Success, and Marketing to improve merchant value, consumer engagement, conversion, and sustainable GMV growth.

Lead Toast’s global People Analytics function across data engineering, analytics, data science, and employee listening. Set the people data and reporting strategy, establish trusted metrics and governance, partner with executives on workforce decisions, measure People program impact with Finance, and build scalable self-service analytics. Manage a distributed US and India team, oversee executive and board-level workforce analysis, and collaborate with privacy, legal, People technology, AI, and data engineering teams.

Performs data profiling, quality analysis, governance, metadata and lineage management, and Master Data Management support. Collaborates with data owners, stewards, business stakeholders, and technical teams to resolve issues and improve governance processes. Maintains glossaries, dictionaries, controls, metrics, and reports; supports enterprise data initiatives, communications, training, and stakeholder adoption. Provides guidance to less experienced analysts while applying governance standards and documenting business requirements.

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Conduct qualitative and quantitative consumer research to identify needs, trends, and product opportunities. Analyze consumer feedback, translate findings into actionable recommendations, and support product roadmaps, positioning, experience design, and go-to-market planning. Collaborate with Product Development, Marketing, and Design teams while managing research partners and multiple projects.

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.