Data engineers. At most companies the data engineer supports the product. Here the data engineer builds it. At the pipeline companies, like Fivetran and Confluent, that means connectors that pull data out of hundreds of different systems, streams that copy each change from a database as it happens, and the checks that catch bad or missing data. At the companies that sell data, like NIQ and Circana, it means the daily work of collecting retail sales, household panels, filings and web data, cleaning them and reconciling them into a dataset a customer pays for. The postings require Python and SQL above all, then Spark, Kafka, Snowflake and Databricks, and Scala appears almost only in these roles.

Java backend and full-stack engineers. Every one of these products rests on a large server-side platform. Java engineers build the engines of integration suites, the query and report engines of business-intelligence tools, the platforms through which datasets are delivered to subscribers, and the services behind maps and location search. Many of these roles are full-stack, so the postings often require SQL and React alongside Java and Spring Boot. A notable share also require experience of building features with large language models.

GenAI engineers. This is where the analyst is being rebuilt as software. GenAI engineers build tools that take a question in plain language, write the query, read the result and explain it. They build search that summarises company filings, broker research and call transcripts for investors, and agents that write and document data pipelines or work out why a table has gone wrong. The postings require Python, LangChain and FastAPI. These engineers are more common here than at most product companies, and their share has been rising.

Frontend engineers. People meet data through a screen, whether a dashboard, a chart, a visual editor for building pipelines or a map viewer. Frontend engineers build those screens in React and Angular, with attention to design systems and user experience, and they are in steadier demand here than at a typical product company.

Python backend engineers. Some datasets are gathered to order from public websites, and some research tools sit on Python services. Python backend engineers write the scrapers, the extraction code and the services behind these products.

Cloud and DevOps engineers. The pipeline and analytics products increasingly run as cloud services shared by many customers. Cloud and DevOps engineers run them, in about the same proportion as at other product companies.

.NET engineers. The consumer-research and market-intelligence companies, like Euromonitor International, Mobility Global and NIQ, run long-established platforms on the Microsoft stack. .NET engineers maintain and modernise them, usually with an Angular or React front end and SQL Server.

Test engineers. Testers check the platforms with Playwright, JUnit and Selenium. They are a smaller part of hiring here than at most product companies.

Also present. Software engineers with map and GIS knowledge work on the map itself, and integration specialists connect data platforms to the systems around them.

Rare here. C/C++ systems engineering, firmware, mobile, SAP and ServiceNow barely appear.