Top Senior Data & Analytics Jobs in Atlanta, GA

Manage quantitative analytics consulting projects, including hypothesis development, data collection, modeling, analysis, research, and insight generation. Oversee analysts, collaborate with clients and partners, deliver high-quality project outcomes, manage demanding workloads, and coach team members. The role requires strong quantitative, communication, documentation, and presentation skills, proficiency with analytics software, and regular travel.

The Senior Applications Analyst oversees Business Needs Assessments for Oracle Cloud ERP, optimizing applications and translating business requirements into system designs. Responsibilities include leading documentation, managing the application lifecycle, and collaborating with technical teams to ensure functionality and data integrity.

The Senior Analytics Engineer builds and maintains data models, ETL pipelines, analytics products, business intelligence solutions, and visualizations. The role partners with stakeholders, Data Governance, Data Science, and Data Platform teams to define metrics, improve data quality, support AI and machine learning initiatives, and establish software engineering practices. Additional responsibilities include DataOps, workflow orchestration, technical documentation, business advising, and emergency response support. The position requires advanced SQL and Python skills, data engineering experience, and travel up to 10%.

Owns the enterprise Data Lakehouse platform, including operations, configuration, integrations, monitoring, data validation, issue resolution, vendor management, releases, documentation, security controls, and lifecycle improvements. Translates stakeholder requirements into data solutions, coordinates cross-functional technology teams, guides platform initiatives, and ensures reliable support for business intelligence, reporting, analytics, and AI use cases.

Uses advanced healthcare analytics to identify member and client needs, evaluate programs, develop predictive models and segmentation criteria, measure clinical, utilization, and financial outcomes, and guide product innovation and business strategy. Partners with clinical and business leaders, conducts applied research, develops analytical methodologies, addresses data limitations, and communicates findings through reports, white papers, conferences, and publications.

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.

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.

Lead the design and delivery of scalable batch and real-time data pipelines across AWS, Snowflake, PostgreSQL, MongoDB, dbt, and Kafka. Establish standards for data modeling, governance, security, testing, observability, and reliability. Build trusted datasets and semantic layers for BI, analytics, AI/ML, RAG, and LLM applications. Drive platform optimization, data integration, MDM, technical strategy, mentoring, and cross-functional collaboration.

Analyze complex datasets, build Power BI dashboards and reports, write SQL, and use Python with Pandas and NumPy for data manipulation and automation. Work extensively with Snowflake, validate and reconcile data, define KPIs, support forecasting and AI-driven analytics, and translate stakeholder requirements into analytical solutions. Present insights and recommendations while collaborating with business, data engineering, and data science teams to improve reporting, data quality, governance, and self-service BI.

Designs and maintains production data pipelines, models claims and operational data, and improves reliability through testing, monitoring, and alerting. Owns workflow troubleshooting, backfills, performance optimization, documentation, access controls, and CI/CD practices. Partners with business and technical teams to define requirements and deliver trusted datasets using SQL, Python, cloud data warehouses, and orchestration tools.