AI-Native Data Platform Engineer

Company DescriptionFarther is a rapidly growing RIA that combines expert advisors with cutting-edge technology - delivering a comprehensive, tailored wealth management experience.Farther’s founders are leaders and innovators from the private wealth industry who possess a unique blend of traditional wealth management, fintech, and technology production expertise. We’re backed by top-tier venture capital firms, fintech investors, and industry leaders.Joining Farther means joining a collaborative team of entrepreneurs who are passionate about helping their clients and our teammates achieve more. If you’re the type who breaks through walls to get things done the right way, we want to build the future of wealth management with you.The RoleAs an AI-Native Data Platform Engineer at Farther, you will design and own the canonical data foundations powering our financial AI systems. This role sits at the core of our platform — building the ontology, data contracts, and reconciliation frameworks that enable intelligent agents to operate safely and autonomously.AI systems only perform as well as the structure beneath them. You will architect custodial data pipelines, canonical financial models, and AI-ready schemas that support embeddings, retrieval systems, and agent-driven workflows in a regulated wealth management environment.We are building autonomous agents that reason over and act on platform state — and you will define the data layer that makes that possible.Your ImpactDesign scalable ingestion pipelines across custodians (Schwab, Fidelity, Pershing, etc.) and internal financial systemsBuild and evolve canonical models for accounts, positions, transactions, balances, corporate actions, and household hierarchiesDefine financial data ontology and enforce strong data contracts across servicesImplement reconciliation frameworks and golden-source resolution across multi-vendor datasetsEngineer AI-ready data layers optimized for embeddings, vector search, and RAG architecturesStructure financial datasets to improve prompt reliability and LLM output consistencyArchitect closed-loop, agent-driven systems that monitor, reason over, and autonomously remediate data inconsistenciesImplement observability, lineage, governance, and fine-grained access controls across regulated datasetsThe Ideal Match5+ years building production-grade data platformsDeep SQL expertise and strong Python for data engineeringExperience designing canonical schemas and resolving vendor data inconsistenciesStrong understanding of custodial financial data (positions, trades, balances, performance, corporate actions)Familiarity with embeddings, vector databases, and retrieval architecturesExposure to prompt engineering and structured context design for LLM systemsKnowledge of MLOps fundamentals (versioning, monitoring, reproducibility)Comfortable with AWS data services (S3, Lambda, ECS, Glue, Redshift, OpenSearch) and event-driven orchestrationStrong ownership mindset and systems-level thinkingBonus PointsWealth management or capital markets backgroundExperience integrating OpenAI or Anthropic APIs into production systemsExperience designing retrieval schemas for AI agentsExperience with authorization and policy platforms (e.g., OSO, Auth0)Experience implementing fine-grained access control for AI-driven systemsFamiliarity with GitHub-based CI/CD workflows and automationExperience with data governance, lineage, and compliance controlsWhy Join UsLearn & grow through book clubs, seminars, and peer learning sessionsFull health benefits + 401(k) matching & Roth IRA optionsUnlimited PTOAn amazing collaborative atmosphere between product, design, and engineering to solve hard problems togetherReady to disrupt wealth management? Let's talk!