Top Analysis Reporting Jobs in Atlanta, GA

Build scalable cloud-native data and machine learning solutions using Databricks, Spark, PySpark, and SQL. Develop batch and real-time healthcare data pipelines, enterprise data models, Lakehouse architectures, semantic layers, and AI-assisted analytics workflows. Partner directly with business and clinical stakeholders to discover requirements, prototype solutions, deliver demos, drive adoption, and provide technical leadership. Ensure HIPAA-compliant security, governance, lineage, auditability, and regulatory readiness.

Processes and analyzes financial and structural data from financial institutions, investigates data anomalies, contacts reporters to resolve edit failures, supports data-analysis projects, and assists senior analysts with System-level work groups. The intern must communicate effectively with management, Board staff, and reporting institutions while working under direct supervision.

Analyzes data from multiple sources to answer business questions, identify trends, develop recommendations, and communicate insights to technical and non-technical partners. The role applies statistical and analytical techniques, supports predictive modeling and AI/ML-enhanced analytics, measures recommendation outcomes, and ensures compliant data handling and reporting.

Leads talent intelligence strategy by governing external labor-market data, synthesizing multi-source analytics into executive narratives, defining reporting and AI use cases, and partnering with analytics engineering on Snowflake models. Champions talent intelligence platforms, trains recruiting teams, develops sourcing playbooks and market briefings, and advises leaders on competitor talent, skills, compensation, attrition, location, and hiring strategies.

Processes structured and unstructured data using batch and real-time frameworks, validates data quality, develops scalable data solutions, and supports enterprise data warehouses, big data repositories, business intelligence tools, and deployed data products. Partners with analytics teams on Agile planning, maintains applications, reduces technical debt, and monitors emerging data technologies.

Analyze payment and banking transaction data across multiple sources to identify patterns, develop insights, and support data commercialization. The role includes exploratory analysis, data wrangling, quality checks, requirements development, reference data management, and client delivery. It requires collaboration with business, product, architecture, and engineering stakeholders, plus expertise in payments and banking data. The position is on-site Monday through Friday with approximately 10% travel.

Oversee customer and master data operations in SAP/Torque, ensuring data quality, governance, service continuity, and SLA performance. Lead global process standardization, automation, continuous improvement, and cross-functional initiatives. Manage specialist and super-user networks supporting business users and projects. Develop KPIs, reporting, dashboards, and analytics to identify trends, measure effectiveness, improve operational efficiency, and support data-driven decisions.

Analyzes and resolves complex client and internal data issues through triage, root cause analysis, testing, and cross-functional coordination. The consultant reviews analyst work, manages escalations, documents processes, supports software releases and UAT, processes test and production files, tracks operational metrics, trains team members, and identifies process and technology improvements.

Designs and establishes a governed AWS data architecture, including layered lake or lakehouse models, analytical data layers, reusable ingestion and transformation patterns, and data governance controls. The architect will address PII, access, lineage, residency, and loose coupling from operational systems, while building data products when needed. The role also guides appropriate use of low-code workflows and contextual data layers for efficient AI applications.

Leads end-to-end data engineering initiatives, building scalable pipelines, architectures, and data products. Responsibilities include automation, data transformation, testing, deployment, monitoring, root-cause resolution, AI-enabled product prototyping, stakeholder collaboration, and pipeline quality ownership. The role also mentors junior engineers, contributes to technical product decisions, and grows toward full data product ownership.