The role in practice.
WHAT THE WORK ACTUALLY IS
Building and maintaining pipelines Investigating why a number is wrong Responding to source changes Talking to people about what data means Improving reliability and cost
WHAT PROPORTION IS NEW BUILDING
Less than expected.
WHAT SKILLS MATTER BEYOND TECHNICAL
Asking what decision the data will support Explaining limitations honestly Saying when a request will produce misleading results
WHY THAT LAST POINT MATTERS
Delivering a technically correct answer to a badly framed question is a failure.
WHAT TO ESTABLISH FOR EVERY REQUEST
What decision it informs What precision is genuinely needed How fresh it must be
WHAT TO PUSH BACK ON
Requests for real-time data with no real-time decision Metrics with no agreed definition Dashboards nobody will look at
WHAT TO DOCUMENT AS YOU GO
Where data came from, and what was done to it.
WHAT TO AUTOMATE
Anything you have done manually three times.
WHAT TO BUILD A REPUTATION ON
Numbers that are correct, and problems raised before others find them.
WHAT CAREERS LOOK LIKE
Analytics engineering, closer to business modelling Platform engineering, closer to infrastructure Machine learning engineering Architecture and leadership
