Top Analysis Reporting Jobs in New Orleans, LA

Leads and scales a global data engineering and analytics team while remaining hands-on with architecture, pipelines, data quality, dimensional modeling, dashboards, reporting, and customer-facing deliverables. Owns the analytical data platform supporting clinical, operational, product, financial, and customer data. Partners with cross-functional stakeholders, investigates complex data issues, improves platform reliability and security, and mentors engineers.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Leads complex, cross-functional business intelligence initiatives from strategy and scoping through delivery. Partners with senior leaders across Sales, Marketing, Finance, and Operations to build scalable data products and actionable insights. Establishes standards for metrics, data modeling, governance, quality, and storytelling; designs data infrastructure using modern tools such as dbt. Drives data literacy, mentors analysts, contributes to analytics roadmaps, and ensures data assets are trusted, discoverable, secure, and fit for executive decision-making.

Conduct quantitative research for traders by testing market hypotheses, building and maintaining documented signals, performing event studies and backtests, and validating results against leakage, survivorship bias, transaction costs, and regime dependence. Organize reproducible point-in-time datasets, communicate findings clearly, track signal performance, and use AI tools to accelerate research and coding while verifying outputs.

Lead cross-functional analytics initiatives across product, marketing, audio development, and business operations. Build data models, dashboards, self-serve reporting tools, and actionable company-wide metrics. Analyze activation funnels, A/B tests, growth KPIs, and product health while optimizing the analytics infrastructure. Partner with executives, engineering, product, marketing, and data engineering teams to improve governance, data adoption, and decision-making.

Lead the architecture and implementation of complex cloud-based data solutions for customers. Define strategic data roadmaps, support pre-sales engagements as a data engineering subject matter expert, interview and mentor architects and engineers, and oversee data standards and operating procedures. The role requires extensive AWS data architecture experience across traditional and modern data platforms, including big data, machine learning, generative AI, DataOps, infrastructure as code, CI/CD, and Python automation.

The Analytics Architect will design and implement cloud-based analytics solutions, maintain data pipelines, ensure data quality, and collaborate with teams to enhance operational efficiency.

Leads Magellan Health’s enterprise data and analytics strategy, modernization, engineering, governance, business intelligence, and AI/ML enablement. Oversees data platforms, pipelines, quality, lineage, compliance, and reporting while partnering with clinical, operational, and financial stakeholders. Builds and develops a 30+ person organization, manages a $3M–$5M budget, oversees vendors, and presents investment cases to executive leadership. Drives transformation from a low-maturity environment to a governed, insight-driven enterprise data platform.

Manage Advancement data systems, donor and prospect information, fundraising portfolios, reporting, analytics, gift processing, and reconciliation. Maintain donor database integrity, security, and adoption; conduct prospect research and wealth screening; produce dashboards and KPI reports; train staff; and collaborate with Finance and fundraisers to optimize donor engagement and fundraising operations.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.