Data Systems Engineer
Listed on 2026-07-30
-
Software Development
AI Engineer (Applied/Software), Data Engineering
ALL ROLES
Programmable Compliance Infrastructure for Tokenized Asset Markets
Data Systems EngineerOwn the technical architecture and delivery of Bluprynt's compliance data platform. Hands-on. Ship product daily.
Remote
· EST hours
Full-Time Reports to CEO / Founder
THE OPPORTUNITY
Building the compliance data platform for tokenized assetsBluprynt is the Compliance OS for digital assets—the enterprise-grade infrastructure layer linking off-chain compliance with on-chain identity, policy, and supervisory visibility. Trusted by leaders across Trad Fi and DeFi and built in close alignment with central banks and regulators worldwide, Bluprynt helps issuers, banks, and market participants meet regulatory expectations without sacrificing the speed and modularity of on-chain systems.
The company completed the EU's first MiCA pilot, launched Know Your Issuer (KYI) with Paxos and Circle, partnered with Chainlink on automated on-chain compliance enforcement, and closed an oversubscribed $4.25M Seed round in 2026.
We are hiring a Data Systems Engineer to own the technical architecture and delivery of our data platform. This is a hands-on role. You will write code daily, design systems, and ship product—while translating complex regulatory and product requirements into clean, scalable engineering solutions. Strong English communication skills and the ability to work core EST hours are required.
WHAT WE ARE LOOKING FOR
The barMust-Have:
Technical Foundation
- Python (primary) and Type Script / Node.js Exceptional Python engineering for data-intensive systems;
Node.js/Type Script for backend services, async patterns, and API design at scale. - PostgreSQL schema design
Relational modeling for multi-tenant compliance data, indexing strategy, migration workflows. - API integration architecture
Designing polling, webhook, and event-driven pipelines across multiple external providers (think: RWA.xyz, TRM Labs, Cipher Owl, Chainlink, GLEIF). - Multi-source data ingestion & pipeline architecture
Designing and building ETL/ELT architectures that ingest from multiple heterogeneous sources (APIs, databases, documents, on-chain feeds), process both structured and unstructured data, and execute customizable computation logic with scheduling, retries, and observability. - Schema design for structured vocabularies
Versioned taxonomies, controlled vocabularies, or ontology-like systems with cross-reference resolution.
Must-Have:
Blockchain & Web3
- Smart contract interaction
Reading and writing to on-chain registries, understanding gas mechanics, transaction lifecycle, and multi-chain deployment patterns. - On-chain registry design
Credential issuance/revocation, attestation hash anchoring, IPFS metadata storage.
Must-Have:
Leadership
- Managing external or embedded engineering teams
Influence without direct authority, expectation setting, code review culture, knowledge transfer. - Product-to-engineering translation & technical roadmap ownership
Exceptional ability to decompose ambiguous product requirements into precise, phased engineering specifications; scope negotiation, dependency mapping, and shipping increments with clarity. - Startup velocity
Comfort with ambiguity, bias toward shipping V1 with known limitations, iterating in production.
Must-Have: AI Literacy & Automation
- AI agent orchestration & workflow automation
Actively using LLMs, AI agents, and orchestration frameworks (e.g. Lang Chain, Lang Graph, CrewAI, or similar) to build faster with equal or better quality; must be able to design and deploy multi-agent workflows as a core part of the development process. - AI-assisted testing & quality automation
Leveraging AI tools to automate test generation, validation pipelines, and data quality checks; comfort using AI to accelerate development cycles without sacrificing reliability.
Strong-to-Have
- Regulatory or compliance technology experience (KYC/AML, regulatory reporting, credential/certification systems).
- LLM integration for structured data extraction, taxonomy suggestion, confidence-scored classification, and RAG or retrieval-augmented systems for domain-specific knowledge bases.
- Multi-chain experience beyond EVM (Solana, Aptos, Stellar,…
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