Top Data & Analytics Jobs in Atlanta, GA

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.

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.

Designs and optimizes scalable Azure Databricks data pipelines, Lakehouse architectures, Delta Lake layers, data models, and distributed Spark processing. Partners with analytics and data science teams to deliver reliable datasets for AI initiatives. Develops autonomous agents using Mosaic AI, LangChain, or AutoGen; builds vector search indexes; and implements retrieval, orchestration, evaluation, monitoring, alerting, observability, and governance capabilities.

Designs, builds, and supports secure enterprise data architecture, applications, pipelines, integrations, warehouses, and financial data models. The role enables consolidation, planning, forecasting, reporting, and self-service analytics while ensuring data quality, governance, compliance, observability, and performance. Responsibilities include troubleshooting production issues, optimizing infrastructure costs and workloads, implementing emerging technologies and AI-assisted tools, developing BI metrics, and mentoring teams. The engineer collaborates with Finance, Architecture, Engineering, Operations, and Quality teams on complex implementations.

Analyze large datasets, identify trends, build dashboards and KPI reports, optimize SQL queries, validate data, and deliver actionable business insights. The role partners with stakeholders, engineering, and data science teams; supports predictive and AI-driven analytics; documents metrics and business rules; and presents recommendations to leadership. Required expertise includes SQL, Python, visualization tools, data modeling, cloud data platforms, and Snowflake.

Leads long-range workforce and resource planning for contact center operations. Owns demand forecasting, staffing, capacity, headcount, cross-skill, and scenario models across business lines and channels. Develops advanced predictive and prescriptive analytics, performance metrics, and decision-support tools using complex data sets. Advises executives on resource allocation, service levels, costs, and business performance while governing data quality and analytical processes. Leads capacity planning analysts and drives continuous improvement through automation, machine learning, and improved forecasting methodologies.

The Senior Informaticist analyzes process, operational, and engagement data using AI process mining tools and enterprise data sources. They assess data readiness, define metrics and KPIs, build and maintain Power BI dashboards, create visual narratives and infographics, identify trends and improvement opportunities, and communicate actionable insights. The role partners closely with program managers and operational stakeholders in a cross-functional environment, iterating analyses and visualizations to support decision-making and continuous improvement.

Designs and develops scalable cloud-based data platforms and architectures for consulting clients. Translates business objectives into technical solutions, supports data warehousing, business intelligence, master and reference data management, and develops Big Data platforms using Azure or AWS. Leads client initiatives, establishes architectural governance, creates system diagrams, collaborates with stakeholders and vendors, and mentors data architects and engineers.

Analyzes customer usage, health, churn, and retention data to identify risks and expansion opportunities. Builds telemetry queries, reconciles CRM and platform data, develops cohort and retention analyses, and delivers executive-ready recommendations. Maintains SQL and Python/pandas data foundations in Snowflake, applies data modeling and quality practices, troubleshoots pipeline defects, and collaborates with Customer Success, analytics, product, QA, and engineering teams.