Hire MarTech engineers on evidence, not interviews: how hands-on assessments improve hiring and lift your new hires

The hiring problem every MarTech team knows

You need someone who can model data in Adobe Experience Platform, build a data view in Customer Journey Analytics, ship a journey in Journey Optimizer or rebuild lead scoring in Marketo Engage. You have three ways to judge a candidate, and none of them shows you the work:

• Certifications test recall. A multiple-choice exam can't show whether someone can find out why a profile isn't merging.
• Interviews test talking. In a video call, a strong engineer and a strong talker look the same.
• Take-home tests stopped working once AI assistants arrived. You receive a correct answer and no way to know who produced it.

The cost lands later: a new hire who learns on a client's production sandbox, a launch that slips, a manager who discovers the gaps in month three.

What a hands-on assessment is

AdsBot assessments put the candidate in front of the platform's real work, in a private lab workspace that behaves like the product:

1. You pick the tasks from real implementation work across four labs (AEP, CJA, Journey Optimizer and Marketo Engage), including "fix it" scenarios where something is already broken and the candidate has to find it.
2. The candidate gets one login and a private workspace in each lab. The AI Mentor is switched off, so the work is theirs.
3. One clock starts at their first sign-in and covers every lab. When it runs out, the labs close.
4. The checks are automatic. Each task is a set of checks against what the candidate actually built (the schema, the identity, the dataset, the journey), the same checks for every candidate, with the time each one passed.

You receive a report of every check, passed or failed, and when. No interviewer's mood, no polished answer that hides a gap.

What changes in your hiring

You decide on evidence. "Built a Profile-enabled schema with the right identity in 14 minutes, missed the merge policy" is a different conversation from "seemed confident about XDM".

Every candidate gets the same test. The checks don't know who is being assessed, which makes comparisons fair and defensible.

Your senior people get their time back. They read a report and run a focused follow-up interview on what the report shows, instead of running hours of first-round screening.

Fix-it scenarios reveal real seniority. Building something new from a tutorial is one skill. Finding why an existing setup is broken (lead scoring that never reaches the MQL threshold, a cart-rescue journey that never fires) is what experienced implementers do every week.

Examples by role

• AEP data engineer: model a schema with an identity, enable it for Profile, load data, and make one person resolve across two sources.
• CJA analyst: build a connection and a data view, then answer business questions in Workspace.
• Journey Optimizer developer: build an event-triggered journey with waits, conditions and personalised messages.
• Marketo Engage operations: repair a broken lead-scoring setup and hand off qualified leads.
• Implementation lead: a mix across labs, with a fix-it scenario and a time limit that matches the real job.

After the hire: lift the capability you just hired

Hiring on evidence is half the job. The other half is getting the new person productive on your projects quickly, and knowing when they are.

The same labs that assess a candidate train them afterwards. A team licence gives every new hire their own private workspace, the same graded missions and verifiable credentials, and the trainer or manager sees everyone's progress in one place. People learn on a workspace built for mistakes, not on your client's production sandbox.

For deeper roles, the All-access Gold lab features cover the work that separates good from great on a real implementation:

• shaping untidy source data with Data Prep;
• deriving datasets and enriching profiles with SQL in Data Distiller;
• tracing events hop by hop through the Edge Network, with consent;
• activating audiences from a Real-Time CDP sandbox with a consent policy that keeps out the people who said no.

Privacy, built in

Each candidate works in a private workspace that no one else can see. Their results go only to you. Assessment data is deleted within 12 months of the assessment ending.

How to start

Start with a pilot: pick the tasks for one open role, invite two or three candidates, and compare the reports with your current process. Pricing is on request, because it depends on the roles and the number of candidates.

See how hiring assessments work · Request a pilot · Team licences for your new hires

AdsBot is an independent training provider and is not affiliated with Adobe. Product names are used only to describe the skills tested.