Preparing for a Data Engineer interview at Roku requires a structured approach that balances deep technical mastery with strong communication. You must be ready to demonstrate not just how to write code, but why you made specific architectural choices.

Technical Rigor & Code Quality – Roku expects production-grade code even during live coding rounds. You must write clean, readable, and well-structured code in Python and SQL. Pay close attention to edge cases, error handling, and code comments, as interviewers closely evaluate your attention to detail.

Distributed Systems Intuition – You will be asked deep questions about Apache Spark internals, distributed storage, and cloud infrastructure. Do not just memorize definitions; understand how data flows across a cluster, how to optimize memory management, and how to design pipelines that scale horizontally.

Data Modeling & Architecture – Be prepared to design scalable data models from scratch. You should be comfortable discussing dimensional modeling, schema design for streaming data, and the trade-offs between different storage and query engines depending on the business use case.

Collaboration & Ambiguity – Roku values engineers who can take ambiguous business requirements and translate them into concrete technical specifications. Practice explaining your technical decisions clearly, and demonstrate how you collaborate with cross-functional teams like Product, Data Science, and Platform Engineering.

The interview process for a Data Engineer at Roku is thorough, structured, and designed to evaluate both your immediate technical capabilities and your long-term potential within the engineering organization. The entire process typically takes between three to six weeks from the initial application to the final offer decision.

The process begins with a standard recruiter screen to align on your background, career goals, and compensation expectations. This is followed by one or two technical screening rounds, which are conducted remotely. These screens heavily focus on live coding challenges using the HackerRank platform, where you will solve practical Python and SQL problems under the guidance of a senior engineer.

If you pass the screening stage, you will move to the virtual onsite loop. This loop is highly rigorous and consists of several back-to-back interviews focusing on data structures, system design, big data technologies, and your past project experiences. You will interact with senior data engineers, engineering managers, and occasionally product leaders, giving you a comprehensive view of the team culture and expectations.

The timeline above outlines the standard progression from your initial application to the final offer stage. Candidates should use this timeline to pace their preparation, ensuring they are fully prepared for the intensive live coding and system design rounds that occur in the middle and latter stages of the process. While some teams may adjust the number of rounds slightly based on seniority, the focus on technical execution remains consistent.

To excel in the Roku interview loop, you must understand the specific competencies that interviewers are evaluating in each round. The following sections break down the core technical areas you will face.

SQL and Advanced Data Modeling

This area evaluates your ability to manipulate large datasets efficiently and design data structures that support both fast analytical queries and robust ETL processing. Interviewers want to see that you can write optimized SQL and understand the physical layout of data on disk.

Be ready to go over:

• Window Functions – Mastery of functions like ROW_NUMBER(), RANK(), LEAD(), LAG(), and analytical aggregations over specific partitions.
• Query Optimization – Understanding query execution plans, identifying bottlenecks caused by nested subqueries, and optimizing complex JOIN operations.
• Dimensional Modeling – Designing star and snowflake schemas, handling slowly changing dimensions, and structuring fact tables for high-performance retrieval.
• Advanced concepts (less common) – Optimizing queries for columnar storage formats, implementing incremental data loading strategies, and managing partition pruning in distributed data warehouses.

Example questions or scenarios:

• "Design a dimensional data model to support a real-time dashboard tracking ad campaign performance, impressions, and user conversions."
• "Optimize a slow-running SQL query that joins a 10-billion-row telemetry table with a 5-million-row user profile table."

Python and Algorithmic Coding

You will face live coding challenges designed to test your core computer science fundamentals and your ability to translate logic into clean, bug-free Python code.

Be ready to go over:

• Data Structures – Proficient use of arrays, hash maps, sets, queues, and trees to solve algorithmic problems efficiently.
• Time and Space Complexity – Accurately calculating Big O complexity and optimizing your code to meet strict performance constraints.
• File & Stream Processing – Writing memory-efficient code to parse, filter, and aggregate large text or JSON datasets.
• Advanced concepts (less common) – Implementing graph traversal algorithms (BFS/DFS) to solve dependency resolution problems in data pipelines.

Example questions or scenarios:

• "Given a log file containing millions of API requests, write a Python script to find the top 10 most frequent IP addresses using constant memory."
• "Implement a function to merge overlapping intervals, representing contiguous streaming sessions for a set of users."

Big Data and Spark Internals

At Roku's scale, standard database tools are often insufficient. You must demonstrate a deep, practical understanding of distributed computing frameworks, specifically Apache Spark.

Be ready to go over:

• Spark Architecture – The roles of the Driver, Executors, Tasks, and Stages, and how data is distributed across a cluster.
• Shuffle Operations & Data Skew – Understanding what triggers a shuffle, why it is expensive, and how to mitigate data skew using techniques like salting.
• Memory Management – How Spark allocates memory for execution and storage, and how to debug OutOfMemory errors.
• Advanced concepts (less common) – Tuning Spark serialization (Kryo), optimizing broadcast joins, and understanding the differences between the RDD, DataFrame, and Dataset APIs.

Example questions or scenarios:

• "Explain how you would debug and fix a Spark job that is consistently failing with an OutOfMemory error during a large aggregation step."
• "What are the trade-offs of using broadcast joins in Spark, and how do you configure them programmatically?"

System Design and Pipeline Architecture

This round evaluates your ability to design end-to-end data systems that are scalable, reliable, and maintainable. You will be given an open-ended scenario and asked to architect a complete solution.

Be ready to go over:

• Ingestion Strategies – Designing batch vs. real-time streaming ingestion pipelines using technologies like Kafka, Kinesis, or Spark Streaming.
• Data Quality & Monitoring – Implementing automated data validation checks, alerting mechanisms, and data lineage tracking.
• Storage Tiering & File Formats – Choosing the right storage layer (S3, data lakes, data warehouses) and file formats (Parquet, Delta Lake) based on access patterns.
• Advanced concepts (less common) – Designing self-healing pipelines with backpressure handling, schema evolution management, and multi-region data replication.

Example questions or scenarios:

• "Architect an end-to-end data pipeline to collect, process, and store real-time clickstream data from 50 million active streaming devices for downstream machine learning models."
• "Design a data platform that allows internal analysts to query historical viewing habits while ensuring strict user privacy compliance."