Hiring your first analytics person: what to look for (and what to skip)

I read a job posting last month for a “Senior Analytics Manager” at a 40-person e-commerce company. The requirements included GA4 and GTM expertise, SQL and Python fluency, experience with Tableau and Looker, knowledge of statistical modeling, A/B testing methodology, data warehouse architecture, and “3-5 years of experience building analytics teams.” The salary range was 70-85K.

They were looking for four different people in one body at a junior-to-mid salary. Nobody with all those skills is taking that job. And even if they did, no single person can do all of that well.

This is how most companies approach analytics hiring. They compile every analytics skill they’ve ever heard of, paste them into a job description, and hope someone applies who can do it all. The result is either no qualified applicants, or someone who’s mediocre at everything instead of excellent at the thing you actually need.

I’ve helped about a dozen companies make their first analytics hire over the past few years. The approach that works is much simpler than most people expect.

Where you are determines who you need

Before writing a job description, you need to understand your analytics maturity. Not in some abstract consulting-framework sense. Just answer this: what does your analytics setup look like right now?

Stage 1: Basic tracking. You have GA4 installed, maybe through a Shopify/WordPress plugin. Basic pageview data exists. No custom events. No e-commerce tracking or only partial. GTM either doesn’t exist or has a few tags someone set up once. Nobody really looks at the data. Running an analytics audit is the fastest way to figure out which stage you’re in.

Stage 2: Configured tracking. GA4 is set up properly with custom events. E-commerce tracking works. GTM is in place with organized tags. Someone pulls basic reports. But the data lives in GA4 and there’s no data warehouse, no automated dashboards, limited ability to combine data sources.

Stage 3: Integrated analytics. Data flows from multiple sources into a warehouse. Dashboards exist for different teams. Someone analyzes the data regularly and produces insights. Tracking is documented and maintained.

Most companies hiring their first analytics person are at Stage 1 or early Stage 2. This matters because the skills needed at each stage are completely different.

At Stage 1, you need someone who can build. At Stage 3, you need someone who can analyze. Hiring an analyst when you’re at Stage 1 is like hiring a chef when you don’t have a kitchen.

Role 1: The analytics implementer (hire first)

If you’re at Stage 1 or early Stage 2, this is who you need. Not an analyst. Not a data scientist. An implementer.

What they do

• Set up and configure GA4 properly
• Build and manage the GTM container
• Implement custom event tracking based on business requirements
• Set up e-commerce tracking (if applicable)
• Configure server-side tracking and consent management
• QA all tracking implementations
• Document what’s being tracked and how, ideally using a measurement plan
• Work with developers on data layer implementation

What to look for

Hands-on GTM experience. Can they build a container from scratch? Do they understand triggers, variables, data layers? Ask them to walk you through how they’d implement e-commerce tracking for a product page. If they can explain it clearly, that’s a good sign.

Debugging mindset. Tracking breaks constantly. Browser updates, site redesigns, consent changes, developer deploys. The best implementers are the ones who can look at a broken event in the GA4 DebugView, trace it back through GTM Preview mode, find the data layer issue, and fix it. Ask about a time they debugged a tracking problem. The story should be specific and detailed.

GA4 configuration knowledge. Beyond basic setup. Custom dimensions, content groups, data filters, cross-domain tracking, referral exclusions. These are the configuration details that separate clean data from messy data.

Basic understanding of advertising platforms. They’ll need to implement conversion tracking for Google Ads, Meta, LinkedIn, etc. They don’t need to be a media buyer, but they should understand what a conversion pixel does and how server-side events work.

What you can skip

• Advanced SQL (they won’t need it yet)
• Python or R
• Statistical analysis
• Data warehouse experience
• Team management skills
• Any specific industry experience

Where to find them

These people often come from digital marketing backgrounds. They started running campaigns, got frustrated with bad tracking, and taught themselves GTM and GA4. Some come from web development. A few come through analytics bootcamps. Look for portfolios or case studies showing actual implementations, not just certifications.

Role 2: The analyst (hire second)

Once your tracking is solid and producing reliable data, you need someone to make sense of it. This is your analyst.

What they do

• Build dashboards and reports for different stakeholders
• Analyze data to identify trends, opportunities, and problems
• Answer ad-hoc business questions with data
• Set up and analyze A/B tests
• Combine data from multiple sources (analytics, CRM, advertising)
• Translate data findings into business recommendations

What to look for

SQL proficiency. This is non-negotiable. The analyst needs to query data from BigQuery, a data warehouse, or whatever storage you use. Don’t test this with trick questions. Give them a real dataset and ask them to answer a business question. You’ll see how they think.

Communication skills. The best analyst I’ve ever worked with wasn’t the most technical. But she could take a complex finding and explain it to a marketing director in two sentences. Analytics without communication is just spreadsheets. Ask candidates to present a past analysis. Watch how they structure the story, whether they lead with the insight or bury it in methodology.

Curiosity. Good analysts ask “why” relentlessly. Bad ones deliver the number you asked for and stop. When you describe your business during the interview, a curious analyst will ask follow-up questions about user behavior, funnel steps, or data quality. They can’t help themselves. That instinct is worth more than any technical skill.

What you can skip

• GTM implementation skills (your implementer handles this)
• Data engineering
• Machine learning
• People management

Role 3: The data engineer (hire third)

If your company reaches the point where data from GA4, your CRM, your ad platforms, your backend database, and your product analytics tool all need to flow into one place and stay reliable, you need a data engineer.

What they do

• Design and maintain the data warehouse
• Build ETL/ELT pipelines to move data between systems
• Ensure data quality and consistency across sources
• Optimize query performance
• Set up data governance and access controls
• Work with analysts to model data for reporting

What to look for

This hire is technical. SQL, Python, experience with warehouse platforms like BigQuery or Snowflake, familiarity with tools like dbt, Fivetran, or Airbyte. I won’t go deep here because if you’re at the stage where you need a data engineer, you probably have a technical team that can evaluate candidates.

The one thing I’ll emphasize: hire someone who cares about data quality. A fast pipeline that delivers unreliable data is worse than a slow one that delivers clean data. Ask about how they’ve handled data quality issues in the past. The answer tells you a lot.

Certifications are overrated. Curiosity is underrated.

Google’s GA4 certification is free and takes a few hours. I’ve met people with the certification who couldn’t set up a basic e-commerce implementation. I’ve met people without it who built sophisticated multi-domain tracking setups from scratch.

Certifications show that someone can pass a multiple-choice test. They don’t show that someone can debug a broken data layer at 10pm before a product launch. They don’t show that someone will notice a 15% drop in conversion events and investigate before anyone asks.

What I look for instead:

Problem-solving under ambiguity. Give candidates a scenario: “Conversion numbers in GA4 are 30% lower than what the payment provider reports. How would you investigate?” There’s no single right answer. You’re looking for a systematic approach. Do they check consent mode? Do they compare timestamps? Do they consider ad blockers? The thought process matters more than the conclusion.

Self-teaching ability. Analytics tools change constantly. GA4 has been different every six months since launch. The person you hire needs to keep up without being told to. Ask what they’ve learned recently on their own. If they can describe a recent challenge they solved by reading documentation and experimenting, that’s the person you want.

Attention to data quality. Ask about a time they found bad data and what they did about it. The best people I’ve worked with treat data quality as a personal responsibility. They don’t assume the numbers are right just because the dashboard shows them.

Interview questions that actually work

I’ve stopped asking hypothetical questions and started giving practical exercises. Here are the ones that produce the most signal.

For implementers:

“Here’s a staging site. Open GTM Preview mode and tell me what events fire when you add a product to the cart and complete checkout. What’s missing? What would you change?”

Give them 20 minutes with the actual tools. You’ll learn more than an hour of interview questions. Can they navigate GTM Preview? Do they check the data layer? Do they notice that the currency parameter is missing from the purchase event?

For analysts:

“Here’s a dataset export. This company’s revenue dropped 18% last month. Why?”

Give them a real (anonymized) dataset and 30 minutes. Some candidates will jump straight to conclusions. The best ones will first check data quality. Is the tracking complete? Did the comparison period include a holiday? Is the drop across all channels or concentrated in one? The process reveals everything.

For both:

“Explain GA4’s attribution model to me like I’m a marketing manager who’s frustrated that Google Ads is claiming more conversions than our analytics shows.”

This tests both knowledge and communication. Can they explain a technical concept without jargon? Do they acknowledge that the marketing manager’s frustration is valid? Do they offer a practical path forward?

When to use a consultant instead of hiring

Full-time analytics hires make sense when you have ongoing, daily needs for analytics work. If your situation looks more like this, consider a consultant or agency:

One-time setup. You need GA4 and GTM configured properly, but once it’s done, you only need occasional maintenance. Hiring a full-time person for a 2-month project doesn’t make financial sense. A consultant can set it up, document it, and hand it off. Having a proper tag governance framework in place makes handoffs much smoother.

Specialized expertise. You need server-side tracking with a custom Stape setup, or a complex BigQuery pipeline for marketing attribution. This requires deep expertise that a generalist hire won’t have. A specialist consultant solves it faster and better.

Bridge period. You’re growing and will eventually need a full-time hire, but you’re not ready yet. A consultant can handle the work for 3-6 months while you figure out the right role and find the right person.

Audit and strategy. You want an outside perspective on your analytics setup. An experienced consultant has seen dozens of implementations and can identify problems and opportunities that an internal hire might not recognize.

The wrong approach is hiring a full-time analyst when what you actually need is a one-time tracking implementation. It’s equally wrong to keep using a consultant for daily reporting work when a full-time analyst would be more cost-effective and have better business context.

The honest summary

Your first analytics hire should probably be an implementer, not an analyst. Get the tracking right before you try to analyze the data. Bad data analyzed brilliantly is still bad data.

Write a job description for one role, not four. Be specific about what tools they’ll use and what problems they’ll solve in the first 90 days.

Look for curiosity and debugging instinct over certifications and tool lists. The tools will change. The ability to figure things out won’t.

And if you’re not sure whether you need a hire or a consultant, start with a consultant. They’ll help you figure out what you actually need, and if that turns out to be a full-time hire, they can help you define the role based on real requirements instead of guesswork.