Senior Python Engineer - ETF Investment Technology

• 7+ years in software engineering, including recent ownership of production applications in asset management or capital markets \n

• Strong production Python skills that go beyond notebooks, with pandas, Polars or NumPy \n

• Production PostgreSQL experience \n

• Hands-on AWS experience, for example ECS/EKS, Lambda, S3, RDS, CloudWatch, IAM, Secrets Manager and infrastructure as code \n

• A track record of modernising live, business-critical systems incrementally \n

• Solid API, integration, CI/CD, testing, security and observability practices \n

• The ability to work credibly with front-office users in an onsite, iterative environment \n

• Clear written and verbal communication \n, * Experience integrating Bloomberg or FactSet data and managing entitlements, licensing and lineage \n

• Quantitative finance knowledge, such as portfolio analytics, optimisation, backtesting, risk or attribution \n

• Experience consolidating desk-built tools into an institutionally supported platform \n

• Experience working across teams in the US and UK \n

• Design, build, test and support production-grade Python services and analytical applications on PostgreSQL, Snowflake and AWS \n

• Improve workflows for ETF portfolio construction, basket creation, create-redeem, rebalancing, tracking error, risk analysis, backtesting and tax-aware decision support \n

• Refactor and consolidate existing tools while keeping their embedded business logic intact \n

• Introduce clear APIs, service boundaries, reusable components, observability and strong access controls \n

• Build reliable data flows for market, reference, portfolio, risk and trading data, including Bloomberg and FactSet sources \n

• Improve data quality, reconciliation and lineage \n

• Work iteratively with portfolio managers and quants, releasing working software in small increments \n

• Own production quality through automated testing, CI/CD, root-cause analysis and a support model shared across the US and UK \n

• Use AI-assisted engineering responsibly for code analysis, testing, documentation and modernisation \n

• Document the platform and hand knowledge over to permanent teams \n

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