Hire a Vetted Data & Reporting AI Engineer

What a data & reporting AI engineer actually does

A data & reporting AI engineer turns the numbers scattered across your tools — sales platform, accounting software, ad accounts, spreadsheets — into one report you can trust, delivered on schedule, with a plain-English explanation of what changed and why it matters. The AI layer writes the narrative and flags anomalies; the engineering layer is built so the numbers reconcile.

For small businesses this is deliberately not a data-warehouse project. The right deliverable is usually one automated weekly report and two or three alert rules — built in days, not quarters — with every figure traceable back to its source system.

When a small business needs one

• Someone assembles the Monday report by hand from four browser tabs.
• Two tools show two different revenue numbers and nobody knows which is right.
• Problems (refund spikes, ad overspend, missed SLAs) surface weeks late.
• Investors or partners expect updates that currently take you a day to write.

How to evaluate one: seven interview questions that work

1. "Two of our systems disagree on last month's revenue. What do you do?" The right instinct is reconciliation: define the source of truth per metric, document the differences (timing, refunds, fees), and show both until explained. Anyone who picks a number arbitrarily will bury problems.

2. "How do you make sure the AI-written summary never misstates the numbers?" Strong answer: the narrative is generated from the computed figures — the model never does arithmetic — and every claim links to the metric it describes.

3. "What does your alerting philosophy look like?" Few alerts, high signal, with thresholds set from historical variance. Candidates who promise alerts on everything will train you to ignore them.

4. "Where does the report live and how do we change it later?" Expect delivery into tools you already open (email, Slack, a simple dashboard) and a configuration you own — not a bespoke app only they can edit.

5. "How do you handle a source API changing or a credential expiring?" Monitoring on the pipeline itself: the report must say "ad data missing since Tuesday," never silently show zeros.

6. "What's a reporting project you'd refuse to build?" Good engineers push back on vanity dashboards and 40-metric monsters; they'll argue for the five numbers that drive decisions.

7. "How will you validate the first month of output?" Parallel run against the manual report, discrepancies reconciled in writing, sign-off before the manual process stops.

Red flags

• Leads with tool names instead of your decisions.
• Lets a language model compute or "estimate" figures.
• No pipeline-health monitoring plan.
• Wants six weeks before you see a single number.

How we remove the hiring risk

You don't interview anyone or judge this alone. Engineers in our private network are reviewed for production experience, technical judgment, communication, and the ability to own a focused project end to end. After a free assessment we write a fixed-price scope with plain-language acceptance criteria, then match the project with one vetted engineer and prepare one complete delivery plan for your approval. You pay one milestone at a time before that phase begins, the engineer is paid after you accept the milestone, and a 30-day defect warranty covers the launch.