Senior Vice President, Senior Data and AI Engineer

Lido Advisors•Dallas, TX

About The Position

Act as the 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. Establish organization-wide master data management standards for data quality, lineage, and access controls. 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, including Cursor, Claude Code, Copilot, and ChatGPT, and train staff to use these tools productively. Architect and deploy production-grade Retrieval Augmented Generation (RAG) systems using LLMs to streamline manual, labor-intensive tasks. Establish context-specific semantic layers for key Snowflake data tables to support 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 an MLOps framework within the technical team. Engage key stakeholders in requirements gathering and sprint planning, and maintain documentation for projects and workflows using Jira and Confluence. Create and maintain an Azure Artifacts feed for publishing Python packages containing business logic.

Requirements

Master’s degree in business analytics, computer engineering, or a related STEM field.
At least 4 years of experience in the job or a related position.
Experience with Snowflake, dbt, the Azure suite, Python, SQL, Java, MongoDB, GitHub, Plotly Dash Enterprise, Streamlit, and AI-powered IDEs such as Cursor and Claude Code.
Experience in AI/ML engineering, data engineering, and software development with LLMs.
Experience with data orchestration and governance, RAG system design and evaluation, and MLOps and model lifecycle management.
Project management experience.
Domain expertise in quantitative portfolio construction methodologies, including factor models and alternative data integration.

Responsibilities

Act as primary solutions architect for an enterprise-grade data warehouse system.
Design and enforce an SSOT data model; identify, catalogue, and resolve data silos across operational, financial, and legacy systems.
Establish organization-wide master data management standards for data quality, lineage, and access controls.
Architect data orchestration platform foundations and data architecture across dbt, Snowflake, Azure Data Factory, SQL database, and MongoDB.
Establish platform engineering standards, observability frameworks, and data SLAs for downstream consumers.
Participate in security audits of AI tools such as Cursor, Claude Code, Copilot, and ChatGPT, and train staff on their productivity-enhancing use.
Architect and deploy production-grade RAG systems using LLMs to streamline manual, labor-intensive tasks.
Establish context-specific semantic layers for key Snowflake data tables to support dashboards, AI chatbots, and AI agents.
Define the technical research agenda and ML model selection criteria.
Collaborate with financial engineering teams to devise and test portfolio construction machine learning models.
Set up infrastructure and build an MLOps framework within the technical team.
Gather requirements and plan sprints with key stakeholders.
Maintain project and workflow documentation using Jira and Confluence.
Create and maintain an Azure Artifacts feed for publishing Python packages containing business logic.