Preparing for an interview at Roku requires a structured approach that balances technical practice with strategic product thinking. You should not treat this as a test of memorization, but rather as an opportunity to demonstrate how you think, solve problems, and collaborate with others.

Technical Rigor – You must be highly proficient in SQL and foundational Python. Review standard data structure operations, aggregate functions, window functions, and basic algorithmic optimization to ensure you can write clean, bug-free code under time constraints.

Product and Business Acumen – Understand the streaming television landscape. Be ready to discuss how Roku generates revenue, what metrics drive user engagement, and how ad-tech integrations impact the bottom line.

Communication & Influence – Practice articulating your analytical process out loud. Roku interviewers value candidates who can explain why they chose a specific analytical approach and what the business implications of their findings are.

Collaboration in Ambiguity – Show that you can work effectively in large cross-functional teams. Be prepared to discuss how you manage conflicting stakeholder priorities and how you maintain high analytical standards when requirements are unclear.

The interview process for a Data Analyst at Roku is designed to evaluate both your technical depth and your cross-functional collaboration skills. Candidates can expect a multi-stage process that typically spans several weeks, testing your practical coding abilities, business sense, and behavioral alignment.

The journey begins with an initial recruiter screen to assess baseline qualifications, followed by a rigorous online technical assessment. If you pass the technical screening, you will move on to structured interviews with the hiring manager and key team members, culminating in a comprehensive final loop.

The visual timeline above outlines the typical progression of the Roku hiring loop for analytical roles. Candidates should use this sequence to pace their preparation, focusing heavily on SQL and Python fundamentals in the early stages before transitioning to product case studies and behavioral preparation for the later rounds. While the exact timeline can vary based on team location and business urgency, maintaining a consistent preparation schedule across all key evaluation areas is essential.

To succeed in the Roku interview loop, you must understand the specific competencies being evaluated in each round. The following sections break down the primary evaluation areas, what strong performance looks like, and the key concepts you need to master.

SQL and Database Querying

SQL is the foundational tool for any Data Analyst at Roku. Interviewers want to see that you can write optimized, production-grade queries to extract insights from massive datasets without overwhelming database resources.

Be ready to go over:

• Complex Joins and Subqueries – Understanding when to use outer joins, self-joins, subqueries, and Common Table Expressions (CTEs) for readability and performance.
• Window Functions – Mastering functions like ROW_NUMBER(), RANK(), LEAD(), LAG(), and rolling aggregations over specific partitions.
• Data Aggregation and Filtering conditional aggregation using CASE WHEN, filtering grouped data with HAVING, and handling null values effectively.
• Advanced concepts (less common) – Query optimization techniques, indexing strategies, and understanding execution plans for distributed databases.

Example questions or scenarios:

• "Given a table of user streaming events, write a query to find the session length for each user, defining a session break as any period of inactivity longer than thirty minutes."
• "Calculate the running total of advertising revenue generated per day, partitioned by device type, for the last calendar year."

Python and Scripting Fundamentals

Python is utilized at Roku for data cleaning, automation, and more complex data manipulation tasks that go beyond the capabilities of standard SQL. You are expected to demonstrate strong coding hygiene and problem-solving logic.

Be ready to go over:

• Basic Data Structures – Efficient manipulation of lists, dictionaries, sets, and tuples to store and retrieve data.
• String and Data Manipulation – Parsing unstructured logs, cleaning text columns, and handling date-time conversions.
• Writing Robust Functions – Creating reusable, well-documented functions with proper error handling and input validation.
• Advanced concepts (less common) – Utilizing scientific computing libraries for basic statistical modeling or automated data pipelines.

Example questions or scenarios:

• "Write a Python function that takes a raw log string and extracts specific key-value pairs representing ad impressions, returning them as a structured dictionary."
• "Implement a basic search algorithm to find the first recurring element in a stream of device ID logs."

Product Sense and Case Studies

Roku is a product-driven company. Analysts must possess strong product intuition and the ability to translate ambiguous business questions into structured analytical frameworks.

Be ready to go over:

• Metric Frameworks – Defining North Star metrics, input/output metrics, and guardrail metrics for new feature launches.
• A/B Testing Methodology – Designing experiments, determining sample sizes, understanding statistical significance, and interpreting results.
• Root Cause Analysis – Systematically breaking down macro metric changes (e.g., a drop in active accounts) into granular, actionable components.
• Advanced concepts (less common) – Cohort analysis, user lifetime value (LTV) modeling, and understanding network effects in multi-sided platforms.

Example questions or scenarios:

• "The product team wants to introduce a new autoplay feature on the home screen. How would you design an experiment to measure if this feature increases overall streaming time without causing user fatigue?"
• "We noticed a sudden spike in ad-buffering times in a specific geographic region. Walk me through your step-by-step approach to diagnosing this issue."

Behavioral and Cross-Functional Communication

Technical skills are only half the equation; your ability to work with engineers, product managers, and business executives is critical to your success as a Data Analyst at Roku.

Be ready to go over:

• Stakeholder Management – Navigating differing opinions, managing expectations, and pushback on unrealistic analytical requests.
• Translating Tech to Business – Simplifying complex statistical or technical concepts so that business leaders can make confident decisions.
• Handling Ambiguity – Showing how you drive progress on analytical projects when data is messy, incomplete, or poorly defined.
• Advanced concepts (less common) – Mentoring junior analysts, driving team-wide analytics best practices, and influence without authority.

Example questions or scenarios:

• "Tell me about a time when you discovered an error in an analysis you had already presented to executive leadership. How did you handle the situation?"
• "Describe a project where you had to coordinate with multiple teams with conflicting priorities to deliver a unified data solution."