Top Data & Analytics Jobs in St. Louis, MO

Develops and supports business intelligence dashboards, metrics, reports, visualizations, and information models. Translates stakeholder requirements into scalable analytics solutions using SQL, programming, and BI tools. Partners with technical and business teams to define KPIs, validate data, document metric logic, troubleshoot existing solutions, and improve operational decision-making. The role is fully onsite in Hazelwood, Missouri and requires an active U.S. Secret clearance.

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.

Paid, full-time summer intern supporting billing solutions projects in the telecommunications industry. Responsibilities may include developing agentic AI offer generation, designing and analyzing operational information, communicating findings, and troubleshooting system and data issues in production. The role also includes professional development, networking, mentorship, and cross-functional internship activities.

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.

Lead programmatic analytics for clients and internal teams by analyzing campaign and model performance, designing tests, developing measurement solutions, recommending optimizations, and presenting actionable insights. Partner with Sales, Customer Success, Product, Engineering, and clients to align marketing strategies, reporting requirements, and optimization efforts with business goals.