The questions below represent the types of challenges you will face during your interviews. They are designed to illustrate patterns in our evaluation process rather than serve as a strict memorization list. Focus on the underlying concepts and how you would structure your approach to each problem.

Marketing Analytics and Business Strategy

These questions test your domain knowledge and your ability to apply data to real-world marketing challenges. We want to see how you think about ROI, customer journeys, and pipeline health.

• How would you design a dashboard to track the health of our global marketing pipeline?
• What metrics would you use to evaluate the success of a newly sponsored industry conference?
• If our digital ad spend increased by 20% but lead volume remained flat, how would you investigate the cause?
• Explain the difference between first-touch and multi-touch attribution. When would you use each?
• How do you calculate Customer Acquisition Cost (CAC) and why is it important for our marketing mix?

Technical Execution and SQL

These questions evaluate your hands-on ability to manipulate data and extract accurate insights. Expect to write queries or explain your data structuring logic.

• Write a SQL query to calculate the month-over-month growth rate of marketing qualified leads (MQLs).
• How do you handle duplicate records or missing data when building a foundational marketing dataset?
• Explain the difference between a LEFT JOIN and an INNER JOIN, and provide a marketing use case for each.
• What is a window function in SQL, and how might you use it to find the time elapsed between a user's first website visit and their first demo request?
• Walk me through your process for optimizing a slow-running query that powers a daily executive dashboard.

Behavioral and Stakeholder Management

These questions assess your soft skills, communication style, and cultural alignment. We are looking for empathy, resilience, and the ability to drive cross-functional alignment.

• Tell me about a time you had to explain a complex analytical concept to a non-technical marketing leader.
• Describe a situation where you had competing priorities from different marketing teams. How did you manage expectations?
• Share an example of a time when your data analysis led to a significant change in a business strategy.
• How do you handle a scenario where a stakeholder disagrees with the data you have presented?
• Tell me about a project that failed or did not deliver the expected insights. What did you learn?

Technical Proficiency and Data Visualization

As a Data Analyst, your technical toolkit is the foundation of your impact. You are evaluated on your ability to extract data, manage key datasets, and build dashboards that stakeholders actually want to use. A strong candidate writes efficient queries and designs dashboards that highlight the "so what" rather than just presenting a wall of numbers.

Be ready to go over:

• SQL and Data Manipulation – Writing complex joins, window functions, and aggregations to clean and structure data.
• Dashboard Design – Applying best-in-class visualization techniques to convey insights clearly and intuitively.
• Data Architecture Basics – Understanding how data flows from marketing platforms (like Marketo or Salesforce) into a data warehouse.
• Advanced concepts (less common) – Building automated data pipelines, utilizing Python/R for advanced statistical analysis, or prior experience with Dataiku DSS.

Example questions or scenarios:

• "Explain a time you had to build a dashboard from scratch. How did you decide which metrics to include?"
• "Write a SQL query to find the top three performing marketing channels based on conversion rate over the last quarter."
• "How do you ensure data quality and accuracy in a dashboard that is checked daily by the executive team?"

Stakeholder Communication and Cross-Functional Collaboration

Your technical skills are only as valuable as your ability to communicate your findings. This area evaluates how you partner with marketing leaders to define business questions and present solutions. Strong performance involves demonstrating empathy for your stakeholders, active listening, and the ability to confidently guide non-technical audiences through complex data narratives.

Be ready to go over:

• Requirements Gathering – Translating vague stakeholder requests into concrete analytical projects.
• Handling Pushback – Navigating situations where the data contradicts a stakeholder's intuition or desired outcome.
• Executive Presentation – Summarizing deep technical ad-hoc analyses into a few actionable bullet points.
• Advanced concepts (less common) – Leading analytics training sessions for non-technical teams or driving the adoption of new analytics tools.

Example questions or scenarios:

• "Tell me about a time your data analysis disproved a strongly held belief within the marketing team. How did you handle the conversation?"
• "A marketing manager asks you for a data pull without explaining what they are trying to solve. How do you approach this request?"
• "Describe a strategic project you led that required alignment between analytics, marketing, and sales teams."

Key Responsibilities

As a Marketing Data Analyst, your day-to-day work will revolve around transforming raw data into strategic leverage for the global marketing organization. You will partner closely with marketing stakeholders to identify their most pressing business questions, translating these needs into robust ad-hoc analyses. Your insights will directly inform decision-making for both field and digital marketing teams, ensuring that our campaigns are targeted, efficient, and measurable.

Beyond ad-hoc requests, you will take ownership of strategic, long-term projects designed to grow our analytics ecosystem. This includes developing sophisticated models for pipeline forecasting, conducting deep-dive campaign analytics, and optimizing our marketing and channel mix. You will be expected to proactively identify trends and opportunities that the marketing team might have missed, acting as a strategic advisor rather than just an order-taker.

You will also be responsible for owning and managing key datasets that serve as the single source of truth for the marketing department. A significant part of your role involves designing and building new dashboards using best-in-class visualization techniques. Ultimately, you will have the unique opportunity to build innovative, AI-driven solutions directly on the Dataiku platform, showcasing the power of our own product to drive internal success.

Role Requirements & Qualifications

To thrive as a Data Analyst at Dataiku, you need a blend of sharp technical skills and deep business intuition. The ideal candidate brings a proven track record of working within marketing analytics and a passion for data storytelling.

• Must-have skills – Advanced proficiency in SQL for data extraction and manipulation.
• Must-have skills – Extensive experience with data visualization tools (e.g., Tableau, PowerBI, or similar) and a strong portfolio of dashboard design.
• Must-have skills – Prior experience in marketing operations or marketing analytics, specifically dealing with campaign performance, pipeline forecasting, and channel optimization.
• Must-have skills – Exceptional communication skills, with the ability to translate technical findings into actionable business strategies for non-technical stakeholders.
• Nice-to-have skills – Familiarity with the Dataiku platform or a strong desire to learn and build AI-driven solutions using no-, low-, and full-code capabilities.
• Nice-to-have skills – Experience with predictive modeling or scripting languages like Python or R.
• Nice-to-have skills – Background working in a B2B SaaS environment, understanding the nuances of enterprise sales cycles and marketing funnels.

Frequently Asked Questions

Q: How technical is the interview process for the Marketing Data Analyst role? The process strikes a balance between technical execution and business strategy. While you must be highly proficient in SQL and data visualization to pass the technical rounds, the final interviews will heavily weigh your marketing domain knowledge and stakeholder management skills.

Q: Do I need prior experience using the Dataiku platform? No, prior experience with Dataiku is not strictly required, though it is a strong nice-to-have. We are looking for candidates who are eager to learn the platform and leverage its no-, low-, and full-code capabilities to build innovative AI solutions once they join.

Q: What differentiates a good candidate from a great candidate? A good candidate can write a flawless SQL query and build a clean dashboard. A great candidate understands the "why" behind the data request, proactively identifies trends that impact the marketing pipeline, and confidently advises marketing leadership on strategic decisions.

Q: Is this role fully remote? Yes, this specific Marketing Data Analyst position is listed as a US Remote role. You will be collaborating with a global marketing team, so strong asynchronous communication skills and the ability to manage your own time effectively are essential.

Q: How long does the interview process typically take? The end-to-end process usually takes between 3 to 5 weeks, depending on interviewer availability and how quickly you can complete the technical assessment. Your recruiter will keep you updated on timelines at each stage.

Other General Tips

• Master the STAR Method: When answering behavioral questions, strictly follow the Situation, Task, Action, Result framework. Be highly specific about the Action you took and ensure the Result includes quantifiable business impact.
• Understand the B2B Lifecycle: Familiarize yourself with how enterprise software is marketed and sold. Understanding terms like MQL, SQL, Pipeline Velocity, and Conversion Rates will give you a distinct advantage in your business case interviews.