AI-Native Data Platform Engineer

Farther; York and North Yorkshire; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Opportunity to work with a talented 팀

Venture-backed growth

Job summary

Farther seeks an AI-Native Data Platform Engineer to design and own the canonical data foundations powering our financial AI systems. You will architect custodial data pipelines, canonical models, and AI-ready schemas for embeddings, retrieval, and agent-driven workflows in a regulated wealth-management environment.

You will build scalable ingestion across custodians, implement reconciliation, data contracts, governance, and observability to ensure data quality and prompt reliability for LLMs

Qualifications

• 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.

Responsibilities

• Design scalable ingestion pipelines across custodians and internal systems.
• Define financial data ontology and enforce data contracts across services.
• Implement reconciliation frameworks and golden-source resolution across multi-vendor datasets.
• Engineer AI-ready data layers for embeddings, vector search, and RAG architectures.

Skills

5+ years experience

prompt engineering

embeddings

vector databases

retrieval architectures

AWS data services

SQL

Python

canonical schemas

MLOps

data governance

access control

wealth management

OpenAI integration

Anthropic integration

GitHub CI/CD

Tools

OpenAI API

Anthropic API

GitHub Actions

OSO

Auth0

OpenSearch

AWS S3

Lambda

Job description

• 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