Preparing for an interview at Tubi requires a balanced approach that combines strong fundamental software engineering skills with deep expertise in distributed data systems. You should treat the preparation process as a comprehensive review of your technical foundations, system design capabilities, and communication style.

Distributed Computing & Spark Mastery – Tubi relies heavily on Spark for batch and streaming workloads. You must demonstrate a deep understanding of how Spark executes jobs, manages memory, and distributes data across a cluster.

Advanced Analytical SQL – The SQL evaluation at Tubi is notoriously rigorous. You should be highly fluent in window functions, complex joins, subqueries, common table expressions (CTEs), and performance tuning techniques for distributed query engines.

Algorithmic Python Proficiency – While you are interviewing for a data role, your core software engineering skills must be sharp. Expect medium-difficulty algorithmic coding challenges where code cleanliness, time complexity, and edge-case handling are heavily scrutinized.

Pragmatic System Design – Be ready to design end-to-end data systems from scratch. You will need to justify your technology choices, explain how your architecture handles component failures, and demonstrate how it scales with explosive data growth.

The interview process for a Data Engineer at Tubi is designed to thoroughly evaluate your technical depth, architectural capabilities, and cultural alignment. Candidates should expect a highly structured, rigorous, and multi-stage evaluation process that tests both theoretical knowledge and hands-on coding ability.

The process typically begins with an initial recruiter screen to align on experience, expectations, and role fit. This is followed by a technical screen with the hiring manager or a senior engineer, which serves as a gatekeeper round. This screen focuses heavily on your hands-on coding abilities, featuring challenging SQL problems and medium-difficulty Python coding exercises.

If you pass the initial technical screen, you will move on to the comprehensive virtual onsite loop. This loop can consist of up to six distinct rounds, each focusing on a different area of engineering. These rounds cover advanced distributed computing concepts (specifically Apache Spark), data system design, coding, and behavioral evaluations. While the technical standards are high, candidates have noted that the interviewers are highly collaborative and deeply knowledgeable in their respective domains.

The timeline module above illustrates the typical progression a candidate goes through from the initial application to the final decision. Candidates should use this timeline to pace their preparation, ensuring they are fully prepared for the intensive technical screen before moving into the multi-round onsite loop. While the overall process is highly structured, the exact number of onsite rounds may vary slightly based on the seniority of the position and the specific team.

Apache Spark & Big Data Processing

At Tubi, data scale is a primary engineering challenge. This evaluation area measures your ability to write efficient, scalable, and optimized Spark applications to process massive datasets.

Be ready to go over:

• DataFrame and SQL APIs – Knowing how to leverage Spark’s structured APIs to perform complex data transformations and aggregations.
• Spark Architecture & Execution – Understanding how Spark translates your code into jobs, stages, and tasks, and how the Catalyst Optimizer works.
• Performance Tuning – Strategies for managing partitions, optimizing joins (such as broadcast joins), handling data skew, and debugging memory issues.
• Advanced concepts (less common) – Custom User Defined Functions (UDFs) optimization, Spark memory management details, and Spark Streaming stateful operations.

Example questions or scenarios:

• "How would you optimize a Spark job that is experiencing a severe bottleneck due to a data skew in one of the join keys?"
• "Explain how a broadcast join works in Spark and discuss the trade-offs of increasing the broadcast threshold."
• "Write a Spark DataFrame pipeline that reads a stream of user events, filters out duplicates, and aggregates viewing hours by user category."

Advanced SQL & Analytical Querying

SQL is a core tool for any Data Engineer at Tubi. The expectations here go far beyond basic queries; you must show mastery in writing highly efficient, analytical SQL to extract insights from massive, complex tables.

Be ready to go over:

• Window Functions – Mastery of functions like ROW_NUMBER(), RANK(), LAG(), LEAD(), and custom window frames.
• Complex Aggregations & CTEs – Structuring readable and highly performant queries using Common Table Expressions and advanced grouping sets.
• Query Optimization – Understanding query execution plans, indexing, partitioning, and how to rewrite inefficient queries to run faster on distributed databases.
• Advanced concepts (less common) – Recursive CTEs, handling semi-structured data formats (JSON, Arrays) directly within SQL, and user-defined analytical functions.

Example questions or scenarios:

• "Given a table of user video play events, write a SQL query to find the median watch time per user session without using built-in median functions if your SQL dialect doesn't support them."
• "Write a query to identify users who watched at least three different content genres within a rolling 7-day window."
• "How would you optimize a query that joins a 10-billion-row facts table with a 50-million-row dimensions table?"

Python & Algorithmic Problem Solving

This evaluation area ensures you possess the fundamental software engineering skills required to write clean, maintainable, and efficient code.

Be ready to go over:

• Data Structures – Efficient use of arrays, hash maps, sets, queues, and trees in Python.
• Time and Space Complexity – Analyzing and optimizing the Big O complexity of your code.
• Data Manipulation Parsing, cleaning, and transforming raw data formats (such as JSON, CSV, or log streams) programmatically.
• Advanced concepts (less common) – Generator functions for memory-efficient data processing, custom decorators, and basic multi-threading or concurrency concepts in Python.

Example questions or scenarios:

• "Implement an algorithm to detect anomalous viewing patterns in a log stream of user requests, optimizing for both time and memory."
• "Write a Python function that takes a list of dictionary objects representing video metadata and returns them grouped and sorted by multiple dynamic keys."
• "Given a stream of user search queries, return the top K most frequent queries in real-time using an optimal data structure."

Data Pipeline & System Design

This round evaluates your ability to architect end-to-end data platforms that are scalable, reliable, and maintainable. You will need to demonstrate systemic thinking and a deep understanding of modern data architecture.

Be ready to go over:

• ETL/ELT Architecture – Designing robust pipelines that handle data extraction, transformation, loading, and schema evolution.
• Orchestration & Workflow Management – Leveraging tools like Apache Airflow to manage complex task dependencies, retries, and backfills.
• Storage & Warehousing – Selecting appropriate storage formats (Parquet, Avro, ORC) and database technologies (S3, Redshift, Snowflake, Cassandra) based on access patterns.
• Advanced concepts (less common) – Designing lambda or kappa architectures, implementing data quality and observability frameworks, and managing cross-region data replication.

Example questions or scenarios:

• "Design a real-time data pipeline to ingest, process, and store user clickstream events from Tubi's mobile and TV apps for downstream ML recommendation models."
• "How would you design a backfill mechanism to reprocess six months of historical viewing data after a major schema change in your ETL pipeline?"
• "Architect a data ingestion framework that guarantees exactly-once delivery semantics from an upstream message queue to a downstream data warehouse."