Top Senior Data & Analytics Jobs in St. Louis, MO

Embed with a core business area to define problems, build metrics, run analyses and experiments, generate actionable insights, and drive product, growth, AI, or partnership outcomes end-to-end.

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.

Lead and grow a BI and analytics team to build scalable, AI-enabled reporting and self-service analytics. Partner with cross-functional stakeholders to deliver executive dashboards, standardize metrics, ensure data quality and governance, and use AI tools to automate insights and accelerate dashboard development.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.

Analyze large, complex datasets to generate actionable insights; build SQL queries and data pipelines; develop predictive models (regression, clustering, forecasting); create and maintain dashboards; ensure data quality and governance; collaborate with stakeholders to translate findings into business recommendations and document work.

Design, build, and own Sardine's internal data platform and billing infrastructure. Implement and maintain ETL/ELT pipelines, warehouse partitioning, access controls, and data governance. Partner with stakeholders to define KPIs, produce dashboards, and ensure data quality for executive, GTM, product, and finance teams.

Analyze portfolio-level organic search performance to identify trends and opportunities, build dashboards and benchmarks, define KPIs, evaluate strategy and product impacts, partner with Product/Data/Engineering, and translate insights into clear recommendations for technical and non-technical stakeholders.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Lead revenue analytics for GTM: own annual operating plan and weekly forecast, build propensity/churn/expansion models, architect the revenue data warehouse and identity resolution, define TAM/ICP, leverage AI to automate insights, and partner cross-functionally to maintain single-source-of-truth revenue metrics.

Own and scale Provable's data platform on GCP: build and optimize ingestion pipelines from blockchain indexers, design privacy-aware data models, manage Postgres and BigQuery, develop high-performance data-serving APIs, ensure data quality and observability, and use IaC (Terraform, Helm) and Kubernetes to deploy and cost-optimize environments.