Notion India Data Engineer Interview: Rounds, Questions and Preparation

What the job involves

Data Engineers build and maintain the pipelines that move and transform data from source systems into warehouses and lakes for analytics and machine learning. In global tech product companies, they work on batch and streaming ingestion, data modelling, orchestration, and data quality, often using cloud services and SQL-heavy workflows. A fresher typically starts by owning small pipeline components, writing SQL transformations, and fixing data issues under senior guidance.

Role and technical knowledge

1. 1

   What is the difference between ETL and ELT, and when would you choose one over the other in a cloud data platform?

   Explain that ETL transforms before loading while ELT loads raw data first and transforms inside the warehouse. A strong answer mentions cloud warehouses like BigQuery or Snowflake making ELT common due to cheap storage and scalable compute.

2. 2

   How would you design a star schema for a product analytics use case, such as tracking user events in a SaaS tool?

   Cover fact tables for events, dimension tables for users, workspaces, and time, and explain grain, surrogate keys, and why denormalization helps query performance.

3. 3

   Explain how you would use window functions in SQL to find the second most recent action per user.

   Mention ROW_NUMBER or RANK partitioned by user and ordered by timestamp descending, then filtering for rank 2. Show awareness of ties and NULL handling.

4. 4

   What are common causes of data pipeline failures, and how do you make a pipeline idempotent?

   List schema changes, late data, API rate limits, and transient network errors. For idempotency, discuss merge or upsert logic, deduplication keys, and writing to partitioned overwrite paths.

Practical scenarios and problem solving

1. 5

   A daily pipeline that loads sales data is running late and downstream dashboards are stale. How do you debug and communicate the issue?

   Walk through checking upstream source availability, job logs, data volume anomalies, and SLA alerts. A strong answer includes a short incident note to stakeholders and a temporary workaround if possible.

2. 6

   You need to ingest data from a third-party API that has rate limits and occasional downtime. How do you design the ingestion?

   Suggest retries with exponential backoff, pagination handling, checkpointing to a staging area, and storing raw responses before transformation. Mention scheduling during low-traffic windows.

3. 7

   How would you handle duplicate records appearing in a fact table after a re-run of a pipeline?

   Explain using a unique key or hash, MERGE or upsert statements, and partitioning by date to overwrite only affected partitions. Mention adding data quality checks to detect duplicates early.

4. 8

   Your Spark job on a large dataset is slow and expensive. What steps would you take to optimise it?

   Cover partitioning and bucketing, avoiding shuffles, using broadcast joins for small tables, caching intermediate results, and tuning executor memory and parallelism.

Behavioural and situational questions

1. 9

   Tell me about a time you had to learn a new technology quickly for a project or internship.

   Use the STAR format and highlight how you broke down the learning, used documentation or a mentor, and delivered a working outcome on time.

2. 10

   Describe a situation where you found a mistake in your own work or someone else's data. What did you do?

   Show ownership by admitting the issue, quantifying the impact, fixing it, and adding a check or process to prevent recurrence. Avoid blaming others.

3. 11

   How do you prioritise when you have multiple data requests from different teams?

   Mention clarifying business impact, deadlines, and dependencies, then aligning with your manager. A strong answer shows you communicate trade-offs rather than silently dropping work.

4. 12

   Why do you want to work as a data engineer at a global product company rather than a services firm?

   Connect your interest to building scalable systems, owning pipelines end to end, and working with product and analytics teams. Show you understand the difference in pace and ownership.

HR and background questions for freshers

1. 13

   Walk me through your resume and explain how your projects or internships relate to data engineering.

   Keep it to two minutes, focus on data-heavy work such as SQL, Python, pipelines, or dashboards, and end with why this role is the natural next step.

2. 14

   What do you know about our company's data stack or products, and how would you contribute in the first 90 days?

   Research the company's public engineering blogs or product pages. Talk about learning their tools, shadowing on-call, and taking small well-scoped tasks first.

3. 15

   Are you comfortable working with ambiguous requirements and asking for clarification from senior engineers?

   Give an example from a college project where you asked questions early. Emphasise that you document assumptions and confirm before building.

4. 16

   What are your salary expectations and notice period as a fresher?

   For a fresher, say you are open to the company's standard entry-level band and focus on learning and growth. Keep notice period factual and aligned with your college or internship timeline.