Senior Vice President, Senior Data and AI Engineer

DUTIES: Act as primary solutions architect for an enterprise-grade data warehouse system. Design and enforce a single source of truth (SSOT) data model by identifying, cataloguing, and resolving data silos across operational, financial, and legacy systems, and establishing master data management standards governing data quality, lineage, and access controls organization-wide. Architect data orchestration platform foundations and establish data architecture across dbt, Snowflake, Azure Data Factory, SQL database, and MongoDB, including platform engineering standards, observability frameworks, and data SLAs for downstream consumers. Participate in security audits of new AI tools such as Cursor, Claude Code, Copilot, and ChatGPT, and train staff on productivity-enhancing use of such tools. Architect and deploy production-grade RAG (Retrieval Augmented Generation) systems using LLMs to streamline manual, labor intensive tasks. Establish context-specific semantic layers for key data tables in Snowflake to support development of dashboards, AI chatbots, and AI agents. Define the technical research agenda and ML model selection criteria and collaborate with financial engineering teams to devise and test new portfolio construction machine learning models. Set up infrastructure and build framework for MLOps within the technical team. Engage with key stakeholders for requirements gathering and sprint planning and maintain documentation for key projects and workflows using Jira and Confluence. Create and maintain Azure Artifacts feed for publishing Python packages containing business logic. REQUIREMENTS: Master’s degree in business analytics, computer engineering, or related STEM field, plus 4 years of experience in job or related position. Must have experience in following Tools: Snowflake, dbt, Azure suite, Python, SQL, Java, MongoDB, GitHub, Plotly Dash Enterprise, Streamlit, AI powered IDEs like Cursor, Claude code. AI/ML engineering, Data engineering, software development with LLMs, Data orchestration and governance, RAG system design and evaluation, MLOps and model lifecycle management, project management, domain expertise in quantitative portfolio construction methodologies, including factor models and alternative data integration. We are an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or any other class protected by federal, state or local law. In addition, we do not discriminate against qualified applicants with arrest or conviction records.

Description DUTIES: Act as primary solutions architect for an enterprise-grade data warehouse system. Design and enforce a single source of truth (SSOT) data model by identifying, cataloguing, and resolving data silos across operational, financial, and legacy systems, and establishing master data management standards governing data quality, lineage, and access controls organization-wide. Architect data orchestration platform foundations and establish data architecture across dbt, Snowflake, Azure Data Factory, SQL database, and MongoDB, including platform engineering standards, observability frameworks, and data SLAs for downstream consumers. Participate in security audits of new AI tools such as Cursor, Claude Code, Copilot, and ChatGPT, and train staff on productivity-enhancing use of such tools. Architect and deploy production-grade RAG (Retrieval Augmented Generation) systems using LLMs to streamline manual, labor intensive tasks. Establish context-specific semantic layers for key data tables in Snowflake to support development of dashboards, AI chatbots, and AI agents. Define the technical research agenda and ML model selection criteria and collaborate with financial engineering teams to devise and test new portfolio construction machine learning models. Set up infrastructure and build framework for MLOps within the technical team. Engage with key stakeholders for requirements gathering and sprint planning and maintain documentation for key projects and workflows using Jira and Confluence. Create and maintain Azure Artifacts feed for publishing Python packages containing business logic. REQUIREMENTS: Master’s degree in business analytics, computer engineering, or related STEM field, plus 4 years of experience in job or related position. Must have experience in following Tools: Snowflake, dbt, Azure suite, Python, SQL, Java, MongoDB, GitHub, Plotly Dash Enterprise, Streamlit, AI powered IDEs like Cursor, Claude code. AI/ML engineering, Data engineering, software development with LLMs, Data orchestration and governance, RAG system design and evaluation, MLOps and model lifecycle management, project management, domain expertise in quantitative portfolio construction methodologies, including factor models and alternative data integration.