Top Senior Analysis Reporting Jobs in Atlanta, GA

Own the architecture, development, and operations of Cargill’s enterprise data platform. Build and optimize Azure and on-premises ETL/ELT pipelines, manage databases and migrations, implement CI/CD, improve performance and reliability, and create embedded Superset dashboards. Monitor platform health, data quality, security, and costs while integrating emerging AI capabilities. Mentor data professionals and make architectural decisions across global teams.

Leads Sales Business Intelligence strategy and AI transformation as a senior individual contributor. Develops enterprise Power BI dashboards, data models, SQL transformations, metrics frameworks, AI agents, and automated analytical workflows. Partners with sales leadership, IT, engineering, security, and governance teams to deliver trusted insights, improve adoption, reduce manual reporting, and influence revenue decisions. Mentors analysts and drives AI-enabled ways of working without direct people-management responsibility.

Leads client-facing data validation and risk workstreams, developing reliable data pipelines, integrations, transformations, and validation controls. Manages project plans, budgets, timelines, deliverables, and team performance while analyzing data structures and system interactions to identify risks. Coaches team members, collaborates with stakeholders, improves database and query performance, promotes automation and AI-enabled tools, and ensures technical standards, documentation, and independence requirements are maintained.

Lead the strategy and governance for GTM analytics across pre-sales, post-sales, solutions engineering, forward-deployed engineering, and professional services. Establish trusted source-of-truth metrics, data models, semantic layers, self-service analytics, and AI-assisted querying. Partner with Data, Finance, Sales, and Services leaders to resolve metric definitions, improve data access, consolidate reporting, and guide cross-functional programs without direct authority.

Leads data ownership and governance for data products, defining metadata, quality, controls, integration, and risk mitigation. Partners with business, analytics, technology, and engineering teams to deliver curated datasets and production-ready solutions. Uses advanced SQL for validation and root-cause analysis, manages workstreams and staff, and supports analytics and machine learning use cases. Applies AI and LLM tooling to automate governance activities and maintains data integrity across regulated environments.

Lead and grow an enterprise data engineering team while owning the Snowflake and Airflow platform roadmap. Build reliable, governed data products and modernize pipelines using AI-assisted and agentic workflows. Establish standards for quality, observability, lineage, cost efficiency, incident response, and documentation while partnering with Analytics, Data Science, Product, and business stakeholders.

Build and maintain scalable batch, streaming, and real-time data pipelines, platforms, data lakes, warehouses, and lakehouses. Implement data quality, governance, security, observability, automation, and cloud modernization. Partner with product, engineering, analytics, data science, and business teams to deliver analytics, AI/ML, reporting, and customer insight solutions. Troubleshoot production issues, guide architecture, mentor engineers, and provide technical leadership across marketing data initiatives.

Leads enterprise data governance strategy, data quality standards, stewardship models, policies, metadata, lineage, classification, access controls, and lifecycle management. Partners with business and technical teams to resolve data quality issues, implement governance platforms, monitor KPIs, communicate risks, and maintain compliance with regulations such as HIPAA and GDPR.

Lead the design and implementation of enterprise-scale Knowledge Graphs and Semantic AI solutions, integrating graph intelligence and Generative AI. Manage cross-functional teams in data architecture and governance.

Coordinates workforce forecasting and analysis processes, developing 6–36-month demand and staffing forecasts. Builds predictive and scenario-based models, monitors accuracy, translates demand into workforce requirements, evaluates initiative ROI, and provides data-driven insights for staffing, budgeting, capacity planning, and strategic decisions. Presents forecasts and risks to leadership while ensuring data quality and alignment with operating plans.