AI-Native Data Platform Engineer

Company Description

Farther 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 Role

As 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 Impact

Design scalable ingestion pipelines across custodians (Schwab, Fidelity, Pershing, etc.) and internal financial systems

Build and evolve canonical models for accounts, positions, transactions, balances, corporate actions, and household hierarchies

Define financial data ontology and enforce strong data contracts across services

Implement reconciliation frameworks and golden-source resolution across multi-vendor datasets

Engineer AI-ready data layers optimized for embeddings, vector search, and RAG architectures

Structure financial datasets to improve prompt reliability and LLM output consistency

Architect closed-loop, agent-driven systems that monitor, reason over, and autonomously remediate data inconsistencies

Implement observability, lineage, governance, and fine-grained access controls across regulated datasets

The Ideal Match

5+ years building production-grade data platforms

Deep SQL expertise and strong Python for data engineering

Experience designing canonical schemas and resolving vendor data inconsistencies

Strong understanding of custodial financial data (positions, trades, balances, performance, corporate actions)

Familiarity with embeddings, vector databases, and retrieval architectures

Exposure to prompt engineering and structured context design for LLM systems

Knowledge of MLOps fundamentals (versioning, monitoring, reproducibility)

Comfortable with AWS data services (S3, Lambda, ECS, Glue, Redshift, OpenSearch) and event-driven orchestration

Strong ownership mindset and systems-level thinking

Bonus Points

Wealth management or capital markets background

Experience integrating OpenAI or Anthropic APIs into production systems

Experience designing retrieval schemas for AI agents

Experience with authorization and policy platforms (e.g., OSO, Auth0)

Experience implementing fine-grained access control for AI-driven systems

Familiarity with GitHub-based CI/CD workflows and automation

Experience with data governance, lineage, and compliance controls

Why Join Us

Learn grow through book clubs, seminars, and peer learning sessions

Full health benefits + 401(k) matching Roth IRA options

Unlimited PTO

An amazing collaborative atmosphere between product, design, and engineering to solve hard problems together

Ready to disrupt wealth management? Let's talk!