Data Engineer, AI & Automation

Sound Income Group LLC•Fort Lauderdale, FL

2h•Onsite

About The Position

Sound Income Strategies, a Sound Income Group company, is a Registered Investment Adviser (RIA) with $4.5 billion in assets under management. The firm provides discretionary and non-discretionary investment management services to independent financial advisors and their clients nationwide. As a fiduciary, Sound Income Strategies is committed to portfolio oversight, regulatory compliance, and disciplined investment implementation aligned with clients’ objectives. Sound Income Strategies is seeking a Data Engineer, AI & Automation to modernize our operational data infrastructure, reporting, and business workflows. Reporting directly to the Chief Technology Officer, this hands-on individual contributor will serve as the primary internal owner of backend data engineering and applied AI automation. This role combines Snowflake expertise, strong data analysis, and production software engineering to consolidate information from custodians, trading platforms, portfolio reporting systems, and other business sources. The ideal candidate can understand how our teams work, identify valuable improvements, and independently build, deploy, and maintain reliable tools that employees use in their daily work.

Requirements

Bachelor’s degree or higher in Computer Science or a related technical discipline.
3+ years of software engineering experience in backend, data-intensive, or workflow automation systems, with demonstrated ability to independently build, deploy, and maintain production solutions.
Strong hands-on proficiency in Snowflake, SQL, and a backend programming language such as Python, including experience with data modeling, APIs, and data pipelines.
Hands-on experience integrating Claude into production business applications, including document retrieval, text extraction, and document understanding.
Practical experience implementing Model Context Protocol (MCP) integrations and working with a managed model platform such as Snowflake Cortex, Amazon Bedrock, Google Vertex AI, or a comparable service.
Demonstrated ownership of production LLM-powered functionality or agentic workflows, including output evaluation, failure handling, cost management, and appropriate human review.
Experience handling sensitive data in a regulated or high-trust environment, with strong analytical, troubleshooting, and data quality skills.
A track record of translating operational needs into practical, maintainable tools, with strong communication, documentation, collaboration, and prioritization skills.

Nice To Haves

Experience in financial services preferred but not required.
Familiarity with RIA operations, custodian data, trading systems, or portfolio reporting platforms.
Experience with cloud-based data aggregation platforms and vendor-supported application environments.
Ability to contribute across the application stack when a business solution requires it.

Responsibilities

Develop and maintain backend data pipelines, integrations, and data models in Snowflake to support accurate, accessible operational data.
Learn and work within the existing data aggregation architecture; integrate and reconcile information from custodians such as Schwab, trading platforms such as Orion, portfolio reporting systems such as Black Diamond, and supplemental sources.
Analyze source data, investigate discrepancies, and implement validation, reconciliation, and quality checks to ensure reporting and downstream applications use complete, accurate information.
Translate complex data into useful business outputs and partner with internal stakeholders and front-end contributors to validate dashboards, reports, and application functionality.
Meet directly with Operations and other SIS teams to understand manual processes, define requirements, and prioritize improvements with the CTO.
Build and deploy backend services, internal tools, and automations that improve operational efficiency and reduce repetitive manual work.
Develop production AI functionality using Anthropic Claude, including document retrieval, text extraction, document understanding, and multi-step workflows.
Implement and maintain Model Context Protocol (MCP) integrations connecting AI applications to approved tools and data sources, with appropriate permissions and human review.
Use managed model platforms such as Snowflake Cortex, Amazon Bedrock, Google Vertex AI, or comparable services to support practical business applications.
Own solutions from requirements and design through testing, deployment, documentation, user adoption, and ongoing support.
Monitor data pipelines and AI workflows; troubleshoot failures, evaluate output accuracy, and improve reliability, performance, and operating costs.
Implement access controls, logging, and appropriate safeguards for sensitive client and financial data in collaboration with the CTO and Compliance.
Participate in vendor and platform implementation meetings, learn existing systems, and coordinate technical dependencies across internal teams and external partners.
Maintain clear technical documentation and user guidance, and communicate priorities, progress, risks, and results to technical and nontechnical stakeholders.