Top Data & Analytics Jobs in Indianapolis, IN

Develops acquisition fraud analytics and strategies for home and auto platforms. Responsibilities include building analytic solutions, creating automated reports and performance summaries, conducting impact analyses, analyzing client and population data, and tracking champion/challenger strategy tests. The role partners with cross-functional stakeholders, presents insights to senior leadership, and supports credit and fraud initiatives using SAS, SQL, Unix, Excel, and VBA.

Supports finance, executive, and operational teams through data extraction, cleaning, analysis, reporting, and business intelligence. Builds scalable data workflows, SQL queries, dashboards, and visualizations using tools such as Power BI and Tableau. Identifies trends, establishes KPIs, delivers ad hoc analyses, ensures data integrity, and communicates actionable insights to technical and non-technical stakeholders to support strategic decision-making.

Builds and maintains data pipelines, ETL/ELT processes, data warehouses, BI systems, and data governance practices. Integrates structured and unstructured data, ensures data quality and security, monitors workflows, optimizes queries and data structures, and translates business requirements into technical solutions. Collaborates with analysts, engineers, data scientists, product teams, and stakeholders to provide reliable data infrastructure and actionable insights.

Analyzes and resolves complex business systems and data challenges involving incentive compensation, territory alignment, quota setting, and sales crediting. Maintains related processes and reporting tools, consults with senior leaders, presents insights, leads cross-functional problem solving, and improves data quality and operational efficiency using SQL, Alteryx, Tableau, and advanced Excel.

Coordinates workforce forecasting and analysis processes, developing 6–36-month demand and staffing forecasts. Builds predictive and scenario-based models, monitors accuracy, translates demand into workforce requirements, evaluates initiative ROI, and provides data-driven insights for staffing, budgeting, capacity planning, and strategic decisions. Presents forecasts and risks to leadership while ensuring data quality and alignment with operating plans.

Lead the redesign and modernization of a legacy database system for a financial services client. Analyze existing architecture, design scalable data models and schemas, optimize tables and stored procedures, implement governance and security controls, and ensure performance, auditability, and regulatory compliance. Partner with stakeholders in an Agile environment, support iterative development, and mentor team members on database modeling best practices.

Leads Azure data engineering initiatives, designing scalable data pipelines, ETL processes, data architectures, and governance solutions. Manages Azure Data Factory, Databricks, Delta Parquet, Azure Data Lake Storage, Profisee MDM, Azure Purview, SSIS, SSRS, Tableau, and SQL Server optimization. Implements real-time data integration, change data capture, CI/CD, automation, and reporting solutions while ensuring data quality, performance, reliability, and compliance. Provides technical leadership and mentors junior data engineering staff.

Build and maintain observability across enterprise data pipelines, including data quality checks, freshness, volume, schema monitoring, alerting, incident triage, root cause analysis, SLA management, and runbooks. Configure tools such as Monte Carlo, Great Expectations, CloudWatch, and Datadog to improve pipeline reliability and data trust.

Paid 10-week data analytics internship supporting the Mortgage Purchase Program. Responsibilities include building Power BI dashboards, cleaning and analyzing SQL and enterprise data, validating reports, documenting processes, supporting data governance, gathering requirements, and presenting insights. Interns collaborate across teams and gain experience in finance and banking while developing SQL, Power BI, communication, and problem-solving skills.

Build and maintain scalable Snowflake data pipelines and dbt-based ELT workflows using SQL and Python. Integrate multiple source systems, implement Medallion Architecture patterns, monitor pipeline reliability, resolve data quality issues, and support performance and cost optimization. Collaborate with analytics, product, and business teams to deliver trusted datasets while contributing to data modeling, governance, testing, documentation, lineage, code reviews, and deployment practices.