.NET engineers. Much of the software that runs an asset manager's portfolios and produces ratings, research and market data was built on the Microsoft stack, and it is still being extended. .NET engineers build and maintain it, mostly at the investment-management platform SimCorp and the ratings and financial-data companies, in C# with Angular, SQL Server and Azure. They are about twice as common here as at most product companies, and they are hired almost entirely with experience.

Java backend and full-stack engineers. Java engineers build the personal-finance and tax products, the clearing and settlement systems behind a trade, and the services of the ratings and data companies. They are the most common opening here, in about their usual proportion. Most roles are full-stack, so the postings require React or Angular alongside Java and Spring Boot, and many also require Python. Java takes the most juniors here.

Software Engineer, Fintech. The hedge funds, the high-frequency traders, the investment-management platforms, like SimCorp and Clearwater Analytics, and the exchange group LSEG hire many engineers under a plain software engineer title. Most of these roles are ordinary backend work in Python and Java on AWS or Azure, where markets are the setting rather than the skill. A smaller part is defined by trading itself, and these postings often require the FIX protocol, the standard language in which trading systems send orders. Together, fintech software engineers are more than twice as common here as at a typical product company, and at the hedge funds they are the main role.

Data engineers. Ratings, indices and market data are data products. Data engineers build the pipelines that collect prices, filings and company data and turn them into what clients read, in Python and SQL with Snowflake and Spark. They are well above their usual weight here, concentrated at the financial-data and ratings companies.

GenAI engineers. Research, ratings and personal finance are full of text that language models can read and write. GenAI engineers build assistants for analysts, research summaries and help for people filing their taxes, in Python with LangChain, SageMaker and the OpenAI API. They are more common here than at most product companies, at the personal-finance app maker Intuit, SimCorp and the data and ratings companies.

Test engineers. A wrong number in a clearing system or a fund's accounts is a serious failure. Testers hold about their usual place overall, and far more than that at the exchanges, like CME Group and ICE, and the clearing house DTCC. They automate in Java with Selenium, JUnit, Cypress and Playwright.

Cloud and DevOps engineers. Cloud engineers are less common here than at most product companies. They run the investment platforms and data services, in Python and Go with Kubernetes, Terraform and both Azure and AWS, and at the hedge funds they also run the trading infrastructure.

Frontend engineers. Fund managers, analysts and advisers spend the day in dense screens of numbers. Frontend engineers build them, mostly in Angular and React with TypeScript, with attention to performance. They are more common here than at most product companies.

Generalist software engineers. Moody's and the investment-accounting company Clearwater Analytics hire many engineers who work across Java, Python and C#, and some C/C++, rather than in one stack.

Also present. Engineers hired for investment-operations knowledge at Clearwater Analytics and DTCC, network engineers at D. E. Shaw, Python engineers at FactSet, Go engineers at the wealth and investing apps, like Dezerv and Rupeezy, Salesforce developers at Morningstar, mobile engineers for the personal-finance apps, like Intuit and Bright Money, integration and platform developers, ServiceNow developers and a few Software Engineer, Security roles at CME Group.

Rare here. SAP and firmware work barely appear.