Top Senior Analysis Reporting Jobs in New Orleans, LA

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Design, build, and enhance executive Power BI dashboards and scalable data models. Develop DAX, Power Query, optimized visuals, KPIs, templates, and governance while iterating with stakeholders to deliver role-based reporting and drill-through navigation.

The Senior Data Modeler designs and maintains data models, supports AI-driven practices, and collaborates on data lifecycle processes. Responsibilities include translating business needs into data structures, ensuring data governance, and supporting analytics and AI use cases.

Own and improve the dbt transformation layer, semantic layer, data observability, and pipeline orchestration while migrating the platform to AWS. Ensure data quality and governance to support clinical AI, collaborate with clinical, operations, and finance stakeholders, and audit, document, and fix existing data pipelines.

As a Senior Data Analyst, you will analyze game telemetry data, translate business needs into insights, and guide technical teams. You will also lead exploratory data analysis and maintain data quality practices.

As a Senior Data Engineer, you'll transform raw data into datasets, design production pipelines, integrate customer data, and collaborate with various teams to ensure data quality and reliability.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Collect and analyze competitive intelligence and conduct due diligence and investigations on investment candidates and related entities. Design data collection methods, use research, network analysis and AI/automation tools to produce high-quality reports and actionable recommendations for senior stakeholders, and improve workflow productivity.

Design, build, and maintain scalable ETL/ELT pipelines and product-level data architectures. Improve pipeline performance and data freshness, lead code/design reviews, mentor engineers, and collaborate with Data Science, Product, and Engineering to make data reliable and accessible.

Lead end-to-end data conversion projects: analyze and profile legacy data, develop mappings and ETL/ELT transformations, validate and load data, resolve quality issues, coordinate cutovers, mentor juniors, and leverage generative AI to accelerate delivery.