Top Analysis Reporting Jobs in Indianapolis, IN

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Develop business intelligence and revenue reporting tools, automate Finance reporting processes, perform ad hoc and quantitative analyses, and translate complex data into actionable insights. The analyst will support company initiatives, present findings to technical and non-technical stakeholders, and provide decision-making guidance across the organization.

Serve as the primary technical escalation point for managed services data and analytics clients. Troubleshoot and enhance Power BI, Fabric, Azure, and other analytics environments; gather requirements; translate business needs into scalable solutions; support client onboarding; maintain documentation; mentor support staff; and partner with client success and consulting teams. The role also contributes to continuous improvement, training, knowledge sharing, and AI-enabled productivity initiatives.

Leads and develops a data engineering team supporting People Analytics and AI-assisted workforce analytics. Designs scalable data models, pipelines, semantic layers, testing frameworks, and governance practices. Remains hands-on with production code, reviews, and technical problem-solving while partnering with People Analytics, business, legal, and compliance stakeholders. Establishes engineering standards across Python, SQL, dbt, Airflow, Snowflake, CI/CD, data quality, observability, privacy, and security.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Build and lead Ashley’s consumer insights capability, including research roadmaps, custom qualitative and quantitative studies, insights repositories, prioritization processes, and executive-ready storytelling. Translate consumer, retail, marketplace, feedback, and business data into actionable recommendations for product, marketing, merchandising, pricing, and growth decisions. Influence cross-functional stakeholders, develop AI-enabled synthesis workflows, and manage one direct report while operating resourcefully with imperfect data.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Lead the design and operation of automated compliance monitoring for consumer lending: translate regulations into rule-based data checks, own exam and audit data requests, triage exceptions, drive automation, steward compliance data, and provide technical mentorship to a supporting analyst. Produce audit-ready, reproducible evidence for regulators, bank partners, auditors, and senior leadership.

Lead advanced analytics for countrywide insurance product programs: build dashboards and monitoring frameworks, conduct deep-dive analyses and predictive modeling, identify systemic process and data gaps, partner with product and cross-functional teams, and influence strategic decisions to improve growth, retention, and profitability.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.