Growth strategy, startups and innovation


Growth and marketing roles have been changing and specialising since the term "growth hacker" first arose. We've previously covered what were the most common growth roles in Europe and what were those hires responsible for. And although it is difficult to determine what roles you need, it is just as difficult to assess candidates and to identify the right one.

We're now starting a new series on how to assess candidates for different types of growth roles. Our starting point is Growth Data Analysts: a tricky hire that needs to have both creative growth components and technical/analytical skills.

In this article, we'll cover:

  • What to expect of a Growth Data Analyst, including their usual responsibilities and requirements.
  • At what stage of your company should you hire one.
  • How to assess candidates for this position, including examples of data exercises and interview questions.

What is a Growth Data Analyst?

A Growth Data Analyst is in charge of the pillar of all growth: data. Their role is, at its core, measuring data and translating insights into actionable product (or marketing) next steps. This hire is ideally a T-shaped data specialist professional that has a broad knowledge on product and growth funnels/loops.

Below, I summarised the most common responsibilities and requirements for the role. We've comprised them based on job posts by European and worldwide technology companies* that are hiring or have hired for Growth Data Analysts.


  • Metric-setting • Work with the product team to develop key metrics (KPIs, OKRs) that can improve decision making. Implement metric literacy at the organisation, aligning all stakeholders to look at the same metrics. Implement and track these metrics.
  • Data integration and governance for product and marketing • Create and manage a data warehouse that unifies data sources. Understanding of attribution, as well as marketing and advertising tools (e.g. Adwords, Facebook Ads, Salesforce, Marketo, Zendesk).
  • Dashboarding • Build and maintain dashboards and reports in order to democratise data usage across organisation.
  • Identify areas of opportunities • Find actionable insights through funnels, cohort analyses, user segmentation, retention analyses and models to help product growth.
  • Data storytelling • Present data-informed findings and insights in a concise and well-communicated manner.


  • Product Growth • Understand growth funnels, growth loops, acquisition and how to measure them.
  • Data Analysis • Advanced experience in SQL, Microsoft Excel and event-based tracking tracking (e.g. Mixpanel, Adjust, Google Analytics, Segment).
  • Dashboarding • Experience with data warehouses (e.g. Snowflake, BigQuery, Redshift) and data visualisation tools (e.g. Looker, Tableau, Google Data Studio).
  • Analytics • Working knowledge of analytics (e.g. significance, regressions, hypothesis testing).
  • Good communication • Cross-functional knowledge and ability to synthesise and present complex analyses in an easy to understand way. English fluency.
  • We've looked into growth-focused data analyst roles from TikTok, Substack, Grubhub, Vimeo, Gitlab, Cleo AI, Affirm and more.

When do you need a Growth Data Analyst?

It depends on your type of business and, most importantly, how you acquire users.

Revenue growth insights for enterprise software, for example, usually comes first hand from prospects (through sales) and customers (through customer success or account managers). For those types of businesses, a Growth Data Analyst won't be a necessary hire until they've hit a certain threshold in number of customers. Only then the data starts to provide insights that can shape product but that can't be gathered from customers directly.

For B2C or bottoms-up B2B SaaS, the story is entirely different. Since they have more customers (and more customers' data) and less contact with their users/leads, this hire is a top priority. Data is the main engine to measure how their marketing is functioning and what's stopping users from activating or up-selling.

Most companies in this segment we work with make their first Growth hire either a Growth Data Analyst, a Product Marketer a Growth Product Manager. What comes first will depend on the skills you have in your founding team.

Assessing Growth Data Analysts

Based on the most common responsibilities and requirements for Growth Data Analysts, these are 3 key areas they need to be assessed on:

Assessing criteria

  1. SQL • How efficiently can they extract data from a relational database? Can they interpret the results?
  2. Product growth • How well do they understand growth funnels, product and metrics? Can they define KPIs?
  3. Data storytelling • How well can they communicate complicate findings? Can they turn insights into actionable next steps?

For the following part, we'll go through how to assess individually each one of those three criteria.

1. SQL

Why it's needed: Being able to extract and interpret data is the core responsibility of a Growth Data Analyst.

Assessing criteria: Code effectiveness, relevant data and edge cases recognition, data interpretation.

How to assess: Live coding interview.

SQL is a programming language for extracting and managing relational databases. On their day-to-day, Growth Data Analysts rely on SQL for several of their activities, including to:

  • Perform ad-hoc analysis, research or investigation (e.g. A/B testing performance, finding cause for drop in engagement, identifying drop-off points in the funnel);
  • Create cohorts based on a certain user activity (e.g. users who created an account through SSO);
  • To create tables or views that are used for data visualisation (e.g. dashboarding).

Growth-first companies, like Facebook, don't just demand SQL knowledge from their Data Analysts, but also from their Growth Marketers. Although marketers don't need to be as advanced as data analysts in SQL, this shows the importance of dominating this skill for growth.

Facebook conducts online coding and whiteboard interviews for SQL. The role of doing this as an interview, instead of a take-home assignment, is to question and understand the logic and rationale behind the interviewee's decisions.

Below, we can find an example of two simple SQL questions that were asked in a Facebook Growth Marketing Analyst interview:


tbl_user_comments: Table containing all of the comments a user receives in a given day


--- tbl_user_comments ---


-- user_id -- Unique identifier for Facebook user

-- ds -- Date stamp in the format yyyy-mm-dd

-- comments -- The total comments a user received on a given date




tbl_user_country: Table containing the country a user is located in


--- tbl_user_country ---


-- user_id -- Unique identifier for Facebook user

-- country -- Home country code of that user



-- Question #1: Return the total number of comments received for each user for the month of June 2018.

-- Question #2: What percent of users in the United States (US) received comments in June 2018?

Growth Data Analyst hires usually are required a more robust SQL knowledge than the example above, but the complexity level of the coding interview will depend on the type of hire you're searching for. We've seen roles that were heavier on the data science (also requiring R or Python) side. But we've also seen roles where the main responsibility was creative product growth: interpreting the data and coming up with actionable product next steps.  In the end, how advanced the hire's SQL needs to be will depend on your organisation's structuren and maturity.

The coding interview is also a good opportunity to ask follow-up questions to understand how the candidate would conduct their day-to-day work. In the Facebook example above, the interviewer could also ask follow-up questions like; "What other data points could influence how many comments a user receives on a certain day? How would you look into those?".

2. Product Growth

Why it's needed: A Growth Data Analyst needs to understand product growth to prioritise their researches and follow-up recommendations.

Assessing criteria: Growth funnel understanding, metric-driven prioritisation, creativity to influence metrics.

How to assess: Case study interview.

The main differentiator between Data Scientists and Growth Data Analysts is the latter's footing on product growth. As we've seen, a Growth Data Analyst is a T-Shaped data specialist that needs to understand how growth funnels and loops work. They should be expected to:

  • Understand how a growth funnel works in order to prioritise based on reach, impact and probability;
  • Predict and identify how different metrics are impacted by each other (e.g. new users x friend requests sent);
  • Understand how/what growth activities (e.g. new creatives for performance campaigns) impact product performance (e.g. activation);
  • Be creative to recommend follow-up product or growth initiatives based on gathered insights.

Due to the role's nature, it's important to assess product growth where data is the starting point. Mode Analytics has product-focused SQL exercises (e.g. "Understanding search functionality") that showcase the type of questions a Growth Data Analyst should be able to answer. For example:

  1. The product manager comes to you and asks if users are happy with the search functionality on your product. How do you quantify that?
  2. What metrics tell us if search is performing well? How do we track those? What other metrics will be impacted if we improve search?
  3. Should we spend time improving search or are we better focusing on other parts of the product? What other parts of the product could bring us the same benefits of improving search? How do you find that out?
  4. What should be improved in the search functionality and why? How do you find these areas of opportunities?

Using a case study like the one by Mode is a good way to dig deep if a candidate understands which data is needed to interpret a product's performance.

3. Data Storytelling

Why it's needed: Insights need to get across to senior stakeholders, product managers and growth marketers to become valuable.

Assessing criteria: Communication, data visualisation, making the complicated easy-peasy.

How to assess: Take home assignment and on-site presentation.

We think it's more important that Growth Data Analysts have stellar Powerpoint skills than complex data modelling capabilities. Why? The reality is that data is complicated and getting insights across a whole organisation is even more. The best Growth Data Analysts are those that can double down what matters the most, be listened to and get product teams to work on what they've found.

Therefore, it's crucial that a Growth Data Analyst can communicate well, simply, directly and clearly. They should be able to:

  • Distill complicated findings in a way that can be understood by the managing team, engineers, marketers and/or product managers;
  • Identify what is the best visual representation (charts, dashboards, flows) to showcase their results;
  • Produce a clear list of recommendations and/or actionable next steps.

To assess how well a candidate can distill their findings, we recommend a take home assignment. By asking candidates to deep dive into data and prepare a presentation, you're assessing how well they communicate their data analysis and product growth knowledge.


Growth Data Analysts are very important and early hires for startups. It's a tricky hire to assess - since they must have both creative growth and analytical skills - but can be done with a combination of coding exercises, take-home assignment and case study interviews.

Are you interested in reading how to assess another type of Growth role - like a Growth Product Manager or Performance Marketer? Then email us your request to

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