Our Culture
CoinShares is an innovative, agile and ambitious organisation. We strive for excellence in everything we do. We are a high performance culture with a focus on:
- Professional and personal integrity
- Curiosity and a deep learning mindset
- Transparency
- Teamwork and collaboration
CoinShares is strongly committed to diversity and inclusion and warmly welcomes candidates from all backgrounds.
The Team
CoinShares deploys discretionary and systematic, computer-driven trading algorithms across digital assets, cryptocurrencies and derivatives. We have a proven and profitable track record in proprietary trading and are building and expanding our market-making and active investment strategies to complement our world-leading ETP & ETF business.
The Engineering team is responsible for all aspects of software development for the firm, including platform engineering, quant engineering, and ML and AI infrastructure and implementation. As part of a nimble team in a growing organisation, you will be collaborating and developing real time solutions with your colleagues on a constant basis.
Our technical stack runs in a microservices architecture with Golang and Python services deployed on AWS alongside a Java/React user interface. We connect with our proprietary platform, MATRIX, to 15+ trading venues managing hundreds of millions of messages and orders per day. You will continue to scale and improve this platform as crypto gains further prominence at the heart of the world financial ecosystem.
Role Profile
CoinShares is undertaking a strategic multi-year modernisation of its data and AI capabilities. As a Data Engineer, you will own the design and delivery of the pipelines and data services that underpin finance automation, corporate reporting, operations, trading, and our public-facing data offering.
This is a hands-on, delivery-focused role for an engineer with deep expertise in Python, Airflow, AWS and SQL/Postgres. You will take work from an ambiguous business requirement through to a production system, engaging directly with finance, operations and product stakeholders to shape scope, agree priorities and land outcomes rather than waiting for a fully specified brief.
You will also build and maintain the internal and external APIs that serve our data, where correctness, latency and uptime are visible to colleagues, clients and the wider market.
We are building an engineering culture in which AI is part of the toolchain. We expect you to bring practical experience of AI-assisted development and to be able to talk concretely about where it has, and has not, paid off in your own projects.
This role suits someone who is curious and willing to learn, picking up unfamiliar tools, technologies and business domains as the platform evolves. Just as important is a pragmatic approach to delivery: shipping useful short-term solutions quickly, while holding a clear view of the target state and moving towards it iteratively. We are a small team, so how easily something can be maintained and supported counts for as much as how quickly it lands.
Responsibilities
Data Pipeline Development
- Design, build and own end-to-end ETL/ELT flows ingesting from APIs, SaaS platforms, file feeds and internal systems.
- Write clean, well-tested Python for production data pipelines and backend services.
- Develop and optimise SQL transformations in Postgres, including reconciliation and finance calculation logic.
- Translate finance and operations requirements into deliverable pipeline work and drive it through to production.
API Development & Data Services
- Build and maintain internal and external APIs serving data to internal systems, client-facing applications and third parties.
- Own the performance, caching, versioning and availability of published endpoints.
- Define API contracts with product and engineering teams as the data offering expands.
Data Modelling & Quality
- Create analytics-ready datasets and domain-oriented schemas for reporting and automation.
- Implement data validation, monitoring and alerting as a standard part of every delivery.
- Keep published datasets documented, auditable and trusted by Finance and Compliance.
- Design for maintainability as everything built must be supportable by a small team.
Delivery Ownership & Stakeholder Engagement
- Work directly with finance, operations, product and trading stakeholders to define requirements and agree priorities.
- Take projects from problem statement to production, managing your own delivery and surfacing risk early.
- Use AI tooling to accelerate development and testing, and share what works with the wider team.
- Deliver iteratively and ship pragmatic short-term solutions that move measurably towards an agreed target architecture.
- Support new product launches and ad-hoc data requirements as they arise.
- Drive the migration and decommissioning of legacy systems.
Experience & Qualifications
- Approximately 6 years of professional software engineering experience, including at least 4 years in a dedicated data engineering role.
- Deep Python skills for building production data pipelines and backend services.
- Strong SQL with substantial hands-on Postgres experience, including query and schema performance tuning.
- Production experience with Airflow (or a comparable orchestrator), building, operating and debugging DAGs you own.
- Understanding of ETL/ELT concepts and data warehouse/lakehouse patterns.
- Hands-on AWS experience (S3, IAM, Aurora/RDS, Lambda or equivalent).
- Ability to write maintainable, production-quality code in a collaborative team.
- Demonstrable use of AI tooling in your development workflow, with concrete examples from current or recent projects.
- Ability to own delivery: scoping ambiguous business requirements, managing stakeholders and shipping without close supervision.
- A pragmatic, iterative approach to delivery: able to ship a working short-term solution while keeping the target architecture in view.
- Strong co
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