Data Analyst Jobs in St. Louis, MO

Conducts data analysis, reporting, forecasting, dashboard development, data integration, and quality auditing. Translates business requests into report specifications, identifies trends and operational opportunities, documents data definitions and business rules, and communicates insights to stakeholders. The role requires SQL, Excel, and data reporting experience, with preferred experience in procurement or supply chain analytics and SAP, ERP, BI, or sourcing platforms.

Analyze complex geospatial datasets using statistical methods, maintain clean and compliant data holdings, build lightweight pipeline-tracking tools, and create visualizations for technical and non-technical audiences. The role supports a geospatial imagery program and requires active TS/SCI clearance, coding proficiency, and experience communicating analytical insights.

Performs data analysis and validation for customer remediation events. Extracts, transforms, and compares data across multiple database systems to verify population accuracy, remediation calculations, completeness, and customer outcomes. Conducts testing, challenges business processes, partners with stakeholders through remediation milestones, and ensures timely, accurate event completion.

Analyzes GEOINT and organizational data to develop visualizations, dashboards, analytic models, risk-scoring tools, forecasts, and business insights. Supports insider-threat detection, system integrity metrics, error and data-transfer analysis, process improvement, policy operations, technical documentation, and stakeholder communications. Requires collaboration with requesters, subject matter experts, managers, and internal teams to clarify objectives and improve performance.

Designs, builds, and maintains cybersecurity log collection, management, and SIEM environments across on-premises and cloud systems. Responsibilities include configuring data pipelines, troubleshooting data flow, storage, and display issues, performing root cause analysis, maintaining uptime and versions, improving processes with automation, documenting systems, training team members, and supporting customers. The role requires occasional on-call work and collaboration across project phases.

Designs, builds, and maintains cybersecurity log collection, management, and SIEM environments across on-premises and cloud systems. Troubleshoots data flow, storage, and display issues; performs root cause analysis; ensures uptime and policy-compliant versions; automates processes; documents systems and changes; trains team members; and supports internal and external customers. The role also includes occasional rare on-call work.

Lead advanced analytics initiatives for operations, including lakehouse data integration, maintenance and work-order analytics, asset reliability modeling, inventory forecasting, anomaly detection, and NLP of technician notes. Build Databricks Apps and production ML workflows using Python, PySpark, SQL, MLflow, and Databricks technologies. Partner directly with operations stakeholders to translate business knowledge into data definitions and self-service analytical tools.

Leads data discovery, profiling, quality assessment, and Source-to-Target Mapping for wealth management data platforms. Analyzes complex financial datasets using advanced SQL, documents business rules and transformations, translates requirements for ETL and data engineering teams, supports data governance and testing, and communicates findings to technical and business stakeholders.

Leads data discovery, profiling, quality assessment, and Source-to-Target Mapping for enterprise wealth management platforms. Analyzes client, portfolio, transaction, holdings, performance, and billing data using advanced SQL; documents business rules and transformations; partners with ETL and data pipeline engineers; supports data governance, validation, testing, and stakeholder communication.

Design, build, and maintain Power BI and Tableau dashboards; analyze large, complex datasets using SQL, Python, and statistical methods; partner with data engineering and business stakeholders to ensure data quality; translate findings into clear, actionable stories and executive-level presentations; support firmwide analytics growth and automation.

Lead data analysis for application migration: consolidate and validate datasets, perform statistical analyses, build dashboards and reports (Tableau/Power BI/etc.), define KPIs/scorecards, present insights to stakeholders, and support pre/post-migration evaluation within Agile teams.