- 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
- 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
Benefits
- Opportunity to work with a talented team of professionals.
- Drive the success of a venture-backed, rapidly growing firm
- 5+ years building production-grade data platforms
- Exposure to prompt engineering and structured context design for LLM systems
- Familiarity with embeddings, vector databases, and retrieval architectures
- Comfortable with AWS data services (S3, Lambda, ECS, Glue, Redshift, OpenSearch) and event-driven orchestration
- Strong understanding of custodial financial data (positions, trades, balances, performance, corporate actions)
- Deep SQL expertise and strong Python for data engineering
- Experience designing canonical schemas and resolving vendor data inconsistencies
- Knowledge of MLOps fundamentals (versioning, monitoring, reproducibility)
- Strong ownership mindset and systems-level thinking
- 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)
- Familiarity with GitHub-based CI/CD workflows and automation
- Experience with data governance, lineage, and compliance controls
- Experience implementing fine-grained access control for AI-driven systems
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