Top Data & Analytics Jobs in St. Louis, MO

Lead analytics for Wealth Solutions: analyze client segmentation, revenue, and operational data; build models for capacity and workforce planning; produce insights and recommendations for senior leadership; partner with BI and Finance to improve advisor productivity, client experience, retention, and revenue growth; support strategic projects and translate complex data into clear presentations.

Manages Finance analytics engineering and automation, building governed Snowflake and SQL data products, consumption layers, reconciliations, controls, workflows, and integrations. Partners with FP&A, Data Engineering, and business teams to translate Finance requirements into reliable technical solutions. Leads lineage analysis, testing, monitoring, documentation, AI-assisted workflows, production support, and team development while maintaining clear ownership between Finance and enterprise data teams.

The Associate Director will lead AHP’s analytics and data products domain, building curated datasets, transformation pipelines, semantic models, dashboards, and automated reporting. Responsibilities include delivering client and enterprise data products, establishing reporting standards, managing production refreshes and documentation, coordinating data engineering resources, and supporting AI initiatives. The role is hands-on, client-facing, and requires technical leadership across distributed project teams.

Leads Lexia’s business analytics and data strategy function, managing analysts and overseeing data engineering alignment. Owns BI strategy, Tableau governance, data governance, self-service dashboards, AI-enabled analytics, metric definitions, and data quality. Partners with Sales, Marketing, Customer Success, executives, and analytics engineering to translate business needs into insights, KPIs, models, and actionable recommendations. Manages analytics tools and vendors while ensuring responsible, secure, and effective use of enterprise data.

Analyze complex data to support business decisions across marketing, finance, product, sales, medical affairs, and customer success. Responsibilities include defining data requirements, developing and automating data pipelines, building interactive dashboards, identifying trends and process improvements, and presenting insights to executives and external partners. The role requires advanced SQL, Python, BI, visualization, and cross-functional analytics experience, with an emphasis on using AI to improve efficiency.

Designs and optimizes scalable data platforms, architectures, and production pipelines supporting AI and machine learning. Responsibilities include Databricks Lakehouse development, batch and real-time processing, data governance, security, monitoring, performance tuning, infrastructure planning, and collaboration with DevOps, MLOps, and cross-functional teams. The role serves as the AI team’s data engineering subject matter expert and ensures reliable, secure, cost-effective data solutions.

Analyze credit portfolio and loan performance, forecast losses, identify risks and opportunities, and develop strategies to improve credit outcomes and origination volume. Use SQL, Python, dashboards, and visualizations to deliver actionable insights. Present recommendations to stakeholders and senior leaders while managing analytical projects, A/B tests, roadmaps, and business priorities independently.

Lead the architecture and hands-on development of an AI-enabled data migration platform for legal software. Own extraction, transformation, orchestration, data quality, observability, and scalable go-live workflows. Partner with product management to define roadmap priorities, lead a focused engineering team, make architectural decisions, contribute code, and apply AI to migration assessment, mapping, validation, and automation.

Provides Tier 1 and Tier 2 remote technical support for hardware, software, peripherals, networks, VPN, and MFA issues. Configures and ships laptops and equipment for new hires, manages accounts and permissions across Google Workspace, Microsoft 365, Slack, and internal systems, and develops knowledge-base documentation and training guides. Supports the company’s North American expansion and remote workforce.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.