Data & ML · Do Directory
Top data engineers, active now
Most AI projects stall on data, not models. Data engineers connect your systems, such as your CRM, accounts, e-commerce platform and spreadsheets, into pipelines that deliver clean, consistent data on a schedule, ready for reporting, machine learning or AI search.
Look for someone who documents what they build, sets up checks that catch bad data early and leaves you with something your team can run without them.
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Questions
Data engineering: common questions
Do we need a data engineer before an AI project?
If your data lives in several systems or spreadsheets that don’t agree, probably yes. A few weeks of data engineering up front often makes the AI work quicker, cheaper and far more reliable.
Do we need a big data warehouse?
Rarely at first. Many small businesses do well with a modest managed database and a handful of scheduled jobs. A good engineer sizes the solution to your data and budget, not to their favourite tools.
What should we ask about running costs?
Ask for an estimate of the monthly cloud and licence costs at your current volume and at double it, and what alerts are in place if costs or failures spike. Surprise bills are a common and avoidable problem.
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