Data Engineer, Coinshares

• 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 communication skills and willingness to learn from others
• Analytical mindset with excellent execution and operational risk awareness
• Delivery focused with proven ability to pro-actively multi-task in a pressurised environment
• Excellent verbal and written communication skills and interpersonal skills
• Demonstrated capability for identifying and managing critical stakeholders, partners and team members
• Team player with the ability to work collaboratively in a cross-functional team environment
• High attention to detail and a passion for creating an exceptional working environment
• Ability to see opportunities for improvement from all kinds of situations and events, for the benefit of the business, the team, and the individual

Desirable

• 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
• 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
• 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
• 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
• 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