Top Data & Analytics Jobs in Indianapolis, IN

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT solutions using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots data issues, documents data flows, and supports governance. Collaborates with product owners, stakeholders, architects, data scientists, and engineers in an agile environment while applying AI-assisted development responsibly.

Leverage data analytics, business intelligence, and Python to deliver strategic insights and tailored solutions for clients. Build reports, dashboards, data pipelines, and support data architecture and quality initiatives. Analyze data from multiple sources to identify trends, improve operations, and inform decision-making. Collaborate with stakeholders and team members while continuously developing technical skills and contributing to process improvements.

Design and build maintainable data pipelines, ETL processes, and data architecture standards. Collaborate with product managers, analysts, and machine learning engineers to translate requirements into automated, scalable solutions. Apply software engineering best practices, asynchronous systems, APIs, streaming, testing, monitoring, and Python and SQL expertise. Help prioritize data initiatives based on business impact and communicate complex technical concepts to technical and non-technical stakeholders.

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.

Lead enterprise-scale Akeneo PIM implementations, including solution architecture, platform configuration, data architecture, API development, and third-party integrations. Collaborate with developers, architects, clients, and stakeholders to deliver digital transformation projects. Ensure data privacy, security, and lifecycle protection while documenting technical solutions, gathering requirements, managing expectations, and mentoring junior team members.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Own implementation, maintenance, and optimization of client eligibility and claims data feeds. Analyze feed performance, troubleshoot discrepancies, produce error reports, test integrations with external partners, and improve internal tools, documentation, and processes to increase automation and reliability.

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.

Own and operate Toast’s enterprise data governance program, including data domain ownership, governance council coordination, policy compliance, business glossary management, metadata standards, catalog adoption, maturity assessments, and data literacy training. The role partners with business stakeholders, data stewards, catalog teams, and leadership to improve data trust, compliance, discoverability, and quality. It requires governance, metadata, SQL, data catalog, program management, and workshop facilitation experience.