Top Data & Analytics Jobs in St. Louis, MO

Lead enterprise data architecture across multiple SaaS products by unifying reporting platforms, data models, and analytics capabilities. Assess current systems, define target-state architecture, establish data governance and quality standards, separate analytical workloads from transactional systems, and develop phased migration roadmaps. Guide database, engineering, and BI teams on cloud platforms, integration, modeling, performance, security, and reliability. Enable trusted cross-product reporting, governed self-service analytics, and data foundations for AI and advanced analytics.

Design and build modern GCP lakehouse architectures, including Python and Spark pipelines, BigQuery ingestion, Kafka CDC, Delta Lake and Iceberg UniForm integrations, Delta Sharing endpoints, Snowflake and Databricks connectivity, data lineage, governed access controls, and semantic-layer alignment. Collaborate cross-functionally, troubleshoot full-stack issues, and deliver reusable data-sharing adapters and end-to-end data features.

Build and lead complex data integration projects from client onboarding through ongoing platform usage. Analyze and resolve data discrepancies, troubleshoot SIS and SSO integrations, develop internal Retool applications, perform code reviews, prioritize backlogs, manage projects, and mentor teammates. The role requires advanced SQL, development expertise, technical leadership, and collaboration with internal stakeholders, with Ruby on Rails, AWS, Git, and K-12 education experience preferred or desired.

Leads audience research, advertising effectiveness, brand lift studies, and first-party audience analysis. Partners with Sales, Creative Strategy, Media Planning, and leadership to develop actionable insights, sales narratives, measurement strategies, presentations, and client recommendations. Manages research vendors, budgets, contracts, and methodologies while scaling research tools, dashboards, benchmarks, and best practices across the organization.

Lead a Salesforce data engineering team to design, deliver, and support Salesforce-to-Salesforce integrations and migrations, data modeling, ETL/transformations, automation, and observability. Manage hiring, performance, and career development; partner with stakeholders; adopt AI/LLM agent tooling to automate workflows and improve data quality; reduce technical debt and document systems and processes.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Builds and maintains scalable data pipelines and cloud infrastructure supporting an AI biomedical research platform. Responsibilities include ingesting, harmonizing, validating, and documenting multimodal neurodegeneration datasets; managing cloud storage, compute, permissions, and costs; implementing de-identification and data governance workflows; supporting research data repositories and AI/ML teams; and maintaining data lineage, catalogs, and technical documentation. The role requires strong Python and SQL skills, software engineering discipline, cloud experience, and neurodegeneration research data expertise.

Build and maintain product analytics dashboards, validate and QA event tracking, translate event data into analytics-ready datasets, run funnels/cohorts/retention analyses, support A/B testing, and surface actionable product insights to Product, Growth, and Data stakeholders.

Design, build, and maintain data pipelines, dashboards, and predictive models. Analyze large datasets to generate actionable insights, collaborate with stakeholders, ensure data quality and governance, and document/share analytics best practices to support Marketing, Product, Operations, and Workforce teams.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.