Top Data & Analytics Jobs in Indianapolis, IN

Lead growth analytics for Zendesk’s product-led growth motion, guiding a team of analysts and partnering with Product, Engineering, Sales, Marketing, and Data teams. Define funnel metrics, measurement plans, experiments, and instrumentation across acquisition, activation, conversion, retention, and expansion. Analyze customer behavior using segmentation, cohorts, funnels, experimentation, and statistical methods. Improve self-service reporting, dashboards, metric definitions, reusable datasets, and analytics capabilities while coaching analysts and influencing senior stakeholders.

Supports sales analytics by analyzing sales data, uncovering insights, assisting with pipeline management, building visual dashboards, and ensuring data accuracy and usability. Collaborates with cross-functional teams on projects that inform sales strategy and performance. This is a full-time, 12-week internship based at the company headquarters in Carmel, Indiana.

Data Engineering Interns build data pipelines, models, semantic models, and dashboards using Microsoft Fabric, Snowflake, SQL, Python, and Power BI. Responsibilities include ingesting and profiling data, improving data quality, translating business questions into reusable datasets, developing data agents, refining reports, and presenting project findings to the Data & Analytics team.

Administer Lilly’s enterprise Microsoft Fabric and Power BI platforms across development, QA, and production. Responsibilities include deployments, upgrades, high availability, disaster recovery, gateway and dataset performance optimization, governance, auditing, capacity planning, automation, monitoring, troubleshooting, and cloud integrations across Azure, AWS, Snowflake, and Databricks. The role also develops PowerShell and REST API automation, supports security and networking integrations, mentors administrators, establishes procedures, and serves as the platform subject matter expert.

Leads Northwest Bank’s enterprise data governance program, including data policies, quality rules, lifecycle management, metadata, master data, stewardship, governance tooling, and remediation. Facilitates Data Working Groups, investigates root causes, manages governance metrics and reporting, demonstrates tools, documents processes, and supports training. Partners across a matrixed organization to ensure data assets meet standards, controls, regulatory requirements, and business needs.

Integrate operational data into enterprise data warehouses; design, optimize, and support SSIS ETL pipelines; develop SSRS and Power BI reporting solutions and reusable data models; deliver data migrations and self-service extracts. Design production AI/ML solutions using Python, OCR, AWS Bedrock, and Claude, including multi-agent LLM implementations. Partner with stakeholders, lead code reviews, mentor developers, and support production BI deployments.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Leads medium- to large-scale analytics projects and client relationships while conducting data analysis, managing data infrastructure, building reports and calculators, and preparing client presentations. Provides strategic insights, oversees deadlines and quality, mentors analysts, collaborates with data science and technical teams, and communicates findings to technical and non-technical stakeholders.

Leads data engineering for a functional area by defining future-state architecture, establishing technical standards, mentoring team members, developing code and procedures, fixing bugs, maintaining quality, gathering requirements, and supporting application functionality. The role serves as a subject matter expert, partners with architecture and application engineering leaders, and drives technology adoption and professional development.

Assess legacy on-premises infrastructure and design a cloud-native data architecture for AccuWeather For Business. Create current- and target-state designs, modernization roadmaps, sequencing plans, technical specifications, and risk mitigations. Partner with engineering and data teams to implement scalable, secure, resilient backend data systems while maintaining reliable enterprise data delivery. Contribute hands-on code, data models, schemas, capacity estimates, cost analysis, and architecture decisions.