AI-Native Data Platform Engineer, Farther

• 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

Desirable

• 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