How to structure and hire a marketing analytics team

Updated: October 2026. Rewritten for the people who build marketing analytics teams rather than those applying to them, and merged with three older posts on analyst careers, data and analytics roles, and job boards.

A marketing analytics team works when every role maps to a decision the business has to make, and when someone owns the data those roles depend on. Hire for the gap between the questions your marketing team asks and the data it can trust, not for a list of job titles.

This post is for the CMO, head of analytics or head of performance who has to decide which roles the team needs, where it reports, what to screen for and who to hire first. If you're on the other side of the table, the same map tells you what a good team expects from you.

Start from the decisions, not the job titles

Job titles in marketing analytics overlap. A "marketing data analyst" in one company does what a "digital marketing analyst" does in another, and job boards list both for the same work.

Before you write a job description, list the recurring decisions marketing makes and what each one needs:

Budget allocation across channels: reliable campaign data, attribution or marketing mix modeling, and someone who can explain the trade-offs.
Campaign and channel optimization: fast access to performance data, clean UTM and tracking, and analysts close to the channel teams.
Customer and market understanding: research, segmentation and customer experience data.
Process and tooling: martech workflows, data integration and the operations behind reporting.

Each group of decisions points to a role. The roles you can't cover today are your hiring plan.

The roles in a marketing analytics team

These are the roles that recur in marketing analytics job listings and in my earlier posts on the field. Not every team needs all of them, and in a small team one person covers several.

Role	Question it answers	Core skills
Marketing data analyst	What do the trends and patterns in our data say?	SQL, spreadsheets, statistics, visualization
Digital marketing analyst	How are our online channels performing, and what should change?	Web analytics tools, paid media and SEO data, reporting
Marketing research analyst	What do customers prefer, and where is the market going?	Research design, surveys, market data
Marketing operations analyst	Where do our marketing processes and tools slow us down?	Workflow analysis, martech platforms, CRM data
Marketing strategy analyst	Which long-term bets fit the business objectives?	Business modeling, forecasting, communication

Marketing data analyst

The generalist of the team. Marketing data analysts find trends and patterns across sources and turn them into recommendations. I wrote more about how the role has changed in the modern marketing data analyst.

Digital marketing analyst

Specialized in online campaigns: website traffic, search rankings, email, social media and online advertising. They measure what each digital channel delivers and recommend how to improve engagement and return on investment. This role sits closest to performance and channel teams.

Marketing research analyst

Gathers and interprets data on consumer preferences and market trends. Their work feeds product development and positioning more than weekly reporting.

Marketing operations analyst

Works on the efficiency of marketing processes: analyzes workflows, finds bottlenecks and recommends changes, usually with marketing and sales teams. In many companies this role also owns the martech stack's day-to-day health.

Marketing strategy analyst

Uses data for the long-term picture: which markets, segments or channels to build for. It needs a strategic mindset and the ability to look past this month's numbers.

The implementation owner

Most lists of analytics roles leave out the person who makes the data trustworthy in the first place: whoever owns the tracking design. Someone has to translate what the business wants to measure into events and parameters, keep that spec current and sign off releases. That is the owner of the solution design document (SDD), usually a solution architect, a technical lead or a senior analytics engineer. Without that person, every analyst you hire spends part of the week checking whether the data is broken.

The data and analytics roles around the team

The marketing analytics team doesn't work alone. Gartner's research "What Are the Essential Roles for Data and Analytics?" describes the wider data and analytics (D&A) roles a marketing team depends on.

Chief data and analytics officer (CDAO): aligns the data strategy with business goals and makes sure D&A assets support the company's digital opportunities.
Machine learning engineer: builds the AI and machine learning platforms that predictive models, recommendations and some targeting run on.
Data steward: enforces data governance policies and manages information assets, including compliance with governance and security requirements. For marketing, this is the role that protects the integrity of customer and campaign data. See data governance best practices for what they enforce.
Hybrid roles: analytics, business intelligence and data science are converging into roles that combine IT and business skills.
Citizen data scientists: business users with strong data skills who apply data science tools to their own questions. In marketing, they extend what a small central team can cover.

For a head of analytics, the practical question is which of these exist in the company already, and which ones marketing would have to build on its own.

Where the team sits

There are three common ways to place a marketing analytics team, and each one fails in a different way.

Centralized: one team, usually under the head of analytics or a central data function, serving all of marketing. Standards and data quality are easier to hold. The risk is distance: channel teams wait in a queue and build their own spreadsheets.
Embedded: analysts sit inside channel or product teams and report to their leads. Answers are fast and close to the business. The risk is that each team defines metrics its own way, and nobody owns the shared data.
Hybrid (hub and spoke): a small central team owns standards, tracking, tooling and governance; analysts embedded in the teams do the day-to-day analysis. It needs clear ownership of who defines a metric and who can change tracking.

Whatever the model, write down who owns the tracking plan, the metric definitions and the reporting layer. A RACI per area avoids the most common conflict: two teams reporting different numbers for the same KPI.

The skills to screen for

The skills that separate a good marketing analyst are a mix of technical, marketing and communication ability. Screen for all three.

Technical skills

Data analysis tools: spreadsheets and SQL as a baseline; Python or R for larger datasets, automation or modeling.
Analytics platforms: hands-on experience with Google Analytics 4, Adobe Analytics or a product analytics tool, including how the data is collected, not just how the reports look.
Data visualization and reporting: turning findings into dashboards and reports that stakeholders read.
Search and paid media data: an understanding of SEO and SEM data for digital marketing roles.

Marketing understanding

Knowledge of consumer behavior, market research and digital marketing strategy. It is what lets an analyst connect a finding to a business goal and make a recommendation, rather than describe a chart.

Communication

Analysts have to explain complex findings to non-technical stakeholders, clearly and briefly. It decides whether an analysis changes anything.

Judgment with imperfect data

Problem-solving and attention to detail. Analysts constantly face incomplete data and unexpected trends. Small errors in large datasets lead to wrong conclusions, so you want someone who checks before presenting.

Data governance and privacy

Marketing data increasingly falls under regulations such as GDPR and CCPA. Analysts who understand consent, data collection and compliance reduce risk for the whole team.

Where the work is going

Several areas are changing what marketing analytics teams do, and they affect the profiles you hire next:

AI and machine learning, which change how teams analyze data and predict customer behavior.
Predictive analytics for forecasting demand, pricing and inventory, which needs statistics and modeling skills. My post on the marketing data scientist covers this profile.
Customer experience analytics, which joins data from many touchpoints across the customer journey.
Data integration and management: combining sources into a single view of the customer, which needs warehousing and governance skills.
Privacy and compliance, as regulation shapes what marketing can collect and how.

None of these replaces the core analyst. They change which second or third hire makes sense.

How to hire: sourcing, screening and growing people

Where to look

In the US, Glassdoor, Indeed and SimplyHired list marketing analytics roles across experience levels, and Glassdoor's salary and company reviews help you benchmark an offer before you publish it. LinkedIn groups, industry webinars and the analytics community are other places to find and meet candidates.

Illustration of a group of candidates in front of a board listing marketing analytics job requirements — Job boards list the same work under several titles. Start from the decision the role supports.

What to test

Degrees in marketing, business, statistics or data science give a foundation, but they don't show how someone works with real marketing data. Better signals:

A portfolio or case study of past projects, with the problem, the data, the analysis and what changed.
An interview question on impact: how the candidate used data to inform a marketing decision, and what happened to the campaign afterwards.
A short exercise with your own type of data, including a flaw such as a tracking gap or a duplicated event, to see whether they notice it.

Certifications such as Google's Analytics certification, HubSpot Academy or the Digital Marketing Institute show familiarity with tools. They don't show judgment, so treat them as a tiebreaker.

Growing the analysts you have

The tools change every year, so continuous learning is part of the job, not a perk. Budget time and money for courses, conferences and industry events; it's how analysts stay current and how you keep them. Internships and entry-level roles are also a way to grow your own analysts when experienced hires are hard to find.

What to check this week

List your five most important marketing decisions and write next to each one who in the team provides the data behind it. The blanks are your hiring plan.
Name the owner of your tracking design and metric definitions. If there isn't one, that role comes before the next analyst.
Read your open job description against the skills above. Remove tools nobody will use, and add the decision the role is accountable for.