At a Glance
- Tasks: Design and own data foundations for cutting-edge financial AI systems.
- Company: Join a rapidly growing venture-backed firm with a talented team.
- Benefits: Work remotely, competitive salary, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on innovation and collaboration.
- Why this job: Make a real impact by building autonomous agents in wealth management.
- Qualifications: 5+ years in data platforms, strong SQL and Python skills required.
The predicted salary is between 72000 - 88000 £ per year.
- 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, Open Search) 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 Open AI 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 Git Hub-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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AI-Native Data Platform Engineer employer: Farther
At Farther, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As an AI-Native Data Platform Engineer, you will have the opportunity to work alongside a talented team in a rapidly growing, venture-backed firm, with ample opportunities for professional growth and development in the cutting-edge field of financial AI systems. Our commitment to employee success is matched by our focus on building a supportive environment where your contributions directly impact the future of wealth management technology.
StudySmarter Expert Advice🤫
We think this is how you could land AI-Native Data Platform Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Farther!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like AI-Native Data Platform Engineer at Farther.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Farther.
✨Apply Directly through Our Website
When you find a suitable opening like AI-Native Data Platform Engineer at Farther, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace AI-Native Data Platform Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Farther, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Farther. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Farther
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Farther!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
