Senior Data & Platform Engineer
We are looking for a Senior Data & Platform Engineer to architect and build the foundational infrastructure that powers our AI-native platform. This isn’t a standard "data mover" role, you will be designing the core backend systems, abstractions, and developer workflows that allow our product and AI systems to scale reliably.
You’ll be the engine room of the company, bridging the gap between core backend engineering and modern data infrastructure. If you think in terms of systems rather than just scripts, and you enjoy building "self-serve" platforms that empower other engineers, we want to hear from you.
Requirements
- Experience: 7+ years in Data Engineering, Platform Engineering, or Backend Infrastructure. You have a proven track record of shipping and owning production systems
- Systems Mindset: You think about performance, reliability, and failure modes. You don’t just build pipelines; you build platforms
- Technical Mastery: High proficiency in Golang, SQL, Terraform, and AWS
- Database Depth: Deep experience in data modeling and relational database design
- Startup DNA: You thrive in fast-moving, early-stage environments and are comfortable with the autonomy that comes with it
Nice to have
- Experience with LLMs, RAG, or Vector Search
- Background in Fintech or working within highly regulated industries
- Experience as an early hire at a high-growth startup
Responsibilities
- Architect the Foundation: Design and implement core backend systems and integrations that serve as the backbone of our product
- Build the Data Platform: Own the orchestration, pipelines, and developer workflows. You aren't just running jobs; you’re building the platform that runs them
- Scale for Growth: Create high-performance systems for data ingestion, transformation, and access that remain stable under heavy load
- Master the Data Model: Design APIs and relational models with a focus on performance, sanity (schemas/indexes), and long-term scalability
- Own AI Infrastructure: Support our AI/ML initiatives by building and managing infrastructure for embeddings, vector data, and model pipelines
- Operational Excellence: Use Terraform and AWS to manage containers and cloud services, while improving observability and reliability across the entire stack
- Empower the Team: Build tooling for document processing and normalization to enable self-serve data workflows for product and engineering teams
What will be your next steps?
Quick non-technical conversation
Our initial conversation is a brief, non-technical discussion to understand your background and career aspirations. We're keen to learn about your communication style and how you approach teamwork and decision-making.
60 to 90 minutes technical interview
This in-depth technical assessment, lasting 60 to 90 minutes, is designed to evaluate your specific skills and expertise. We will present you with challenges relevant to our client's requirements.
Client interview
In this stage, you will meet directly with the client for a final technical discussion. This interview will be similar in format to our internal technical assessment, allowing the client to see firsthand how your expertise aligns with their specific project needs and team.
Offer
Congratulations on successfully completing our rigorous evaluation process. We are pleased to extend an offer and recommend you to our clients.