Top Senior Data & Analytics Jobs in New Orleans, LA

Lead quota modeling, territory design, and commission planning; maintain Salesforce data and dashboards; leverage Gong/Clari and AI tools to improve forecast accuracy; produce executive-ready sales performance analysis; collaborate cross-functionally and drive continuous improvement in reporting and data governance.

Develop and support Informatica ETL solutions (70% development, 30% support). Build error-handling frameworks using schedulers (Autosys/Control-M/Tidal). Work with PowerCenter grid, IDQ, MDM, Informatica Cloud; perform performance tuning, Unix scripting, Teradata/SQL development, and data cleansing, standardization, and profiling.

Analyze producers' books using internal and external data to identify cross-sell leads and trends. Generate Power BI/Excel reports, apply data mining and AI methods to surface opportunities, and collaborate with producers and national/regional teams to drive cross-selling initiatives and recommendations.

Own data ingestion, validation, reconciliation, and quality pipelines using Python, advanced SQL, and PostgreSQL. Design schemas, indexes, migrations, automated tests, and CI/CD workflows while handling unreliable third-party feeds. Develop test plans, track defects, establish quality metrics, conduct performance and security testing, and support compliance documentation. Preferred experience includes AWS event-driven architectures, healthcare or payer data, orchestration tools, infrastructure as code, FedRAMP environments, data quality frameworks, and anomaly detection.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data quality processes supporting reporting, analytics, and AI/ML workloads. Maintains and migrates legacy SSIS and SQL Server systems, optimizes pipeline performance, and supports complex SSRS reporting. Collaborates with business users, analysts, and IT teams to define data requirements, troubleshoot issues, document solutions, and advance AI-assisted engineering practices.

Lead the design and implementation of scalable data models, transformation pipelines, semantic layers, and analytics engineering solutions. Drive dbt best practices, testing, CI/CD, data governance, quality improvements, performance optimization, and technical standards. Collaborate with analysts, engineers, stakeholders, and clients to translate business requirements into reliable data architectures. Mentor peers, lead technical implementations and delivery workstreams, reduce technical debt, and contribute to shared tooling and frameworks.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Lead end-to-end analytical initiatives on partner enrollment funnels, ensure data quality, build reporting pipelines with BI/engineering, use AI-assisted tools to accelerate analysis, translate insights into strategic recommendations for leadership, and coach growing analytics team members.

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.

Lead end-to-end delivery and technical architecture for concurrent clinical and commercial data programs. Guide requirements, data modeling, MDM design, ETL/ELT pipelines, performance planning, testing, UAT, data quality, governance, and production readiness. Champion AI-assisted engineering, define operational guardrails, and coordinate stakeholders, dependencies, priorities, acceptance criteria, and program governance across business and technology teams.