Top Senior Data & Analytics Jobs in New Orleans, LA

The Principal Data Platform Engineer leads the architecture and delivery of data platforms using Databricks, focusing on data pipeline design and client relationships.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Leads AlphaSense’s Financial Services channel research coverage across banking, insurance, and fintech. Develops coverage roadmaps, channel-check frameworks, expert source strategies, and structured questionnaires. Guides analysts and operations teams on project execution, expert targeting, feasibility, and quality. Acts as the primary financial services domain expert, identifies new research opportunities, maps value chains, and partners cross-functionally to improve scalable AI-enabled research workflows and deliver differentiated, actionable insights.

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.

Own client-facing analytics and attribution: establish baselines, implement tracking and lead-to-sale matching, run holdout and lift experiments, quantify cost avoidance, build cross-client benchmarks, and translate findings into clear ROI narratives that inform pricing, product, and renewal conversations.