Top Analysis Reporting Jobs in Atlanta, GA

Serve as the strategic analytics partner to GTM leadership, connecting Finance, Revenue Operations, Product, Growth, and sales leaders. Build trusted analytics infrastructure, define metrics and reporting, develop scenario models, and translate complex business decisions into executive recommendations. Own recurring business reviews and the end-to-end view of sales performance across routes to market, helping leaders evaluate product, pricing, compensation, organizational, and market changes.

Supports sustainability data analytics through AI proof-of-concepts, data pipeline and QA work, reporting, dashboard maintenance, data quality investigations, process automation, and documentation. The intern will help develop future-state pipelines, standardize reports, support environmental data collection, and ensure reporting aligns with sustainability standards. The role includes mentorship and hands-on experience with SQL, Python, visualization tools, cloud platforms, and environmental data.

Leverage data analytics, business intelligence, and Python to deliver strategic insights and tailored solutions for clients. Build reports, dashboards, data pipelines, and support data architecture and quality initiatives. Analyze data from multiple sources to identify trends, improve operations, and inform decision-making. Collaborate with stakeholders and team members while continuously developing technical skills and contributing to process improvements.

Leads technology, data, automation, analytics, and applied AI solutions supporting investment manager research across public and private markets. Translates business requirements into scalable production systems, develops applications and dashboards, improves data quality and governance, and prototypes AI/LLM capabilities. Partners with research, investment, data, technology, operations, and vendor stakeholders while managing complex initiatives, controls, priorities, and adoption.

Designs and implements scalable enterprise data platforms across cloud, on-premises, and hybrid environments. Builds ETL/ELT pipelines, optimizes SQL and Spark workloads, develops feature-engineered datasets and machine learning workflows, and applies data governance, quality, monitoring, and CI/CD practices. Provides production support, technical documentation, architecture guidance, code reviews, and mentorship to data engineering teams.

Collects, processes, and analyzes moderately complex datasets to identify trends and actionable insights. Builds and maintains automated reports, dashboards, and data visualizations; performs statistical analysis and validation; improves reporting processes; and collaborates with cross-functional, technology, and data engineering teams to ensure data accuracy and support business decisions.

Own and maintain complex datasets, data processes, and client-facing dashboards. Partner with account, strategy, operations, and product teams to deliver data products, identify analytics opportunities, and improve business intelligence capabilities. Lead cross-functional project engagements, developing team processes and client deliverables while translating client requirements into effective data solutions.

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.

Leads large-scale data engineering initiatives, overseeing end-to-end delivery and mentoring junior engineers. Designs scalable, secure data architectures; automates data ingestion, transformation, and deployment; monitors pipelines; and resolves technical issues. The role requires advanced distributed data processing, data modeling, data warehousing, cloud platforms, analytics tools, CI/CD, DevOps, SQL, Python, and data pipeline technologies.