Interview preparation

Nike interview process & preparation

Nike currently has 49+ live openings on OnJob.io, concentrated in Software Engineer, Machine Learning Engineer and Product Manager roles. There are no leaked or company-specific questions here — instead, prepare with the general, role-based interview rounds and most-asked questions for those role types below, then practise them free with OnJob's AI mock interview.

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Honest note: OnJob.io does not publish leaked or company-confidential interview questions. The guides below are general, role-based preparation — the standard rounds and commonly-asked questions for the role types Nike is currently hiring for. Use them to prepare; your real Nike interview may differ.

What to expect when interviewing for Software Engineer, Machine Learning Engineer and Product Manager roles at companies like Nike

Nike is currently advertising these role types on OnJob.io. Each guide is the general interview structure and most-asked questions for that role — prepare with it, then rehearse free with the AI mock interview.

General Software Engineer interview prep

33 live roles at Nike

Coding, data structures, algorithms and system design — the core software engineering loop used by product companies and startups across India.

Typical Software Engineer interview rounds

  1. 1 Online assessment. Timed coding problems on arrays, strings and hashing on HackerRank or HackerEarth.
  2. 2 DSA / problem solving. 1–2 live coding rounds on data structures, algorithms and complexity analysis.
  3. 3 System design. Designing a scalable service (mid/senior); low-level design for early-career.
  4. 4 Hiring manager / culture fit. Past projects, ownership, behavioural questions and team fit.

Commonly-asked Software Engineer questions

  • Reverse a linked list, both iteratively and recursively.
  • Find the two numbers in an array that add up to a target sum.
  • Detect whether a linked list has a cycle.
  • What is the difference between a process and a thread?
  • Explain time and space complexity (Big O) with examples.
  • How does a hash map work internally, and what happens on collisions?
  • Find the longest substring without repeating characters.
  • What is the difference between an array and a linked list?
Full Software Engineer prep AI mock interview

General Machine Learning Engineer interview prep

3 live roles at Nike

ML algorithms, model deployment, MLOps and coding — the ML-engineering interview for AI-first product companies and applied-ML teams.

Typical Machine Learning Engineer interview rounds

  1. 1 ML fundamentals. Algorithms, evaluation metrics, bias-variance and overfitting.
  2. 2 Coding round. Python, data structures and implementing ML logic from scratch.
  3. 3 ML system design. Design a recommendation, ranking or fraud-detection system.
  4. 4 MLOps & deployment. Serving models, monitoring, drift and pipelines.

Commonly-asked Machine Learning Engineer questions

  • Explain the bias-variance tradeoff and how it relates to overfitting.
  • What is the difference between bagging and boosting?
  • How does gradient descent work, and what are its variants (SGD, mini-batch, Adam)?
  • Explain precision, recall, F1 and ROC-AUC, and when to optimise for each.
  • What is regularisation (L1 vs L2) and why does it help?
  • How would you handle an imbalanced dataset in a classification problem?
  • Explain how a transformer / attention mechanism works at a high level.
  • What is the difference between training, validation and test sets, and what is cross-validation?
Full Machine Learning Engineer prep AI mock interview

General Product Manager interview prep

3 live roles at Nike

Product sense, analytical thinking, prioritisation and metrics — the PM interview for product companies, startups and consumer apps.

Typical Product Manager interview rounds

  1. 1 Product sense / design. Design or improve a product for a target user.
  2. 2 Analytical / metrics. Define success metrics, diagnose a metric drop, size a market.
  3. 3 Execution & prioritisation. Roadmaps, trade-offs and stakeholder management.
  4. 4 Behavioural / leadership. Past products shipped, conflict resolution and influence.

Commonly-asked Product Manager questions

  • How would you improve our product (e.g. WhatsApp, Swiggy, or our app)?
  • Design a product for [a specific user group, e.g. delivery riders or senior citizens].
  • What is your favourite product and why? How would you improve it?
  • Daily active users dropped 15% overnight — how do you investigate?
  • How would you decide what to build next with limited engineering resources?
  • What metrics would you track for a feature you just launched?
  • How would you estimate the market size for [a product] in India?
  • Walk me through how you'd prioritise a backlog of 20 feature requests.
Full Product Manager prep AI mock interview

General Data Analyst interview prep

2 live roles at Nike

SQL, Excel, statistics and storytelling with data — the analyst interview for product, BI and analytics teams across India.

Typical Data Analyst interview rounds

  1. 1 SQL round. Joins, aggregations, window functions and query writing.
  2. 2 Excel / spreadsheet round. Lookups, pivot tables and formulas.
  3. 3 Case / analytics round. Business case: define metrics, analyse and recommend.
  4. 4 Tool & viz round. Power BI / Tableau dashboards and stakeholder communication.

Commonly-asked Data Analyst questions

  • What is the difference between INNER JOIN, LEFT JOIN and FULL OUTER JOIN?
  • Write a SQL query to find the second-highest salary in a table.
  • What is the difference between WHERE and HAVING?
  • Explain window functions like ROW_NUMBER(), RANK() and DENSE_RANK().
  • What is the difference between mean, median and mode, and when is each misleading?
  • How do you handle missing or null values in a dataset?
  • Explain the difference between a primary key and a foreign key.
  • What is normalisation, and why does it matter for analysis?
Full Data Analyst prep AI mock interview

General Customer Support interview prep

1 live role at Nike

Communication, empathy, problem-solving and process — the customer-support / customer-success interview for BPOs, SaaS and D2C teams.

Typical Customer Support interview rounds

  1. 1 Communication screen. Spoken clarity, tone and listening.
  2. 2 Scenario / role-play. Handle an angry customer or a tricky query live.
  3. 3 Behavioural. Past support experience, handling pressure and escalations.
  4. 4 Process & tools. CRM, ticketing, SLAs and metrics.

Commonly-asked Customer Support questions

  • How would you handle an angry or frustrated customer?
  • Tell me about a time you turned an unhappy customer into a happy one.
  • What does good customer service mean to you?
  • How do you handle a customer asking for something you can't provide?
  • What would you do if you don't know the answer to a customer's question?
  • How do you stay calm and professional under pressure?
  • Explain the difference between empathy and sympathy in support.
  • How do you prioritise when multiple customers need help at once?
Full Customer Support prep AI mock interview

General Data Engineer interview prep

1 live role at Nike

Data pipelines, SQL, big-data tools and warehousing — the data-engineering interview for analytics, fintech and product teams building data platforms.

Typical Data Engineer interview rounds

  1. 1 SQL & data modelling. Complex queries, window functions, star vs snowflake schema design.
  2. 2 Big data & pipelines. Spark, Hadoop, ETL/ELT design and batch vs streaming.
  3. 3 Coding round. Python/Scala data manipulation and DSA basics.
  4. 4 System design. Design an end-to-end data pipeline or warehouse.

Commonly-asked Data Engineer questions

  • What is the difference between ETL and ELT, and when do you use each?
  • Explain the difference between a data warehouse, a data lake and a data mart.
  • What is the difference between a star schema and a snowflake schema?
  • How does Apache Spark work, and what is an RDD vs a DataFrame?
  • Explain the difference between batch processing and stream processing.
  • What is partitioning and bucketing in Hive, and why do they matter?
  • How do you handle slowly changing dimensions (SCD types 1, 2 and 3)?
  • What is data normalisation, and when would you denormalise for analytics?
Full Data Engineer prep AI mock interview

Rehearse your Nike interview, free

Run a realistic AI mock interview for your target role and get instant feedback on your answers — or take a timed mock test to check your fundamentals before the real thing.

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Nike interview — FAQs

How do I prepare for a Nike interview?

Prepare by role: identify the role you're applying for, then practise the standard interview rounds and most-asked questions for that role type. Nike is currently hiring for Software Engineer, Machine Learning Engineer and Product Manager roles, so focus there. Use OnJob's free AI mock interview to rehearse with instant feedback, and review the role-specific question lists below. These are general role-based questions, not leaked Nike questions.

What questions are asked in a Nike interview?

We don't publish leaked or company-specific Nike questions. What we do provide is the general, frequently-asked interview questions for the role types Nike hires for — such as Software Engineer, Machine Learning Engineer and Product Manager roles. For example, Software Engineer interviews commonly cover: Reverse a linked list, both iteratively and recursively. Find the two numbers in an array that add up to a target sum. Detect whether a linked list has a cycle. Prepare with these general questions and OnJob's free AI mock interview.

How many rounds does a Nike interview have?

It depends on the role. A typical Software Engineer interview runs across 4 rounds — Online assessment, DSA / problem solving, System design, Hiring manager / culture fit. Round counts vary by company and seniority; treat this as the general structure to prepare for, not a guarantee of Nike's exact process.

Is Nike hiring right now?

Yes — Nike has 49+ live openings on OnJob.io, refreshed daily, mostly for Software Engineer, Machine Learning Engineer and Product Manager roles. Browse the live Nike jobs and see your AI match score on each before you apply.

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