Snowflake Data Engineer Interview: Rounds, Questions and Preparation

What the job involves

Data Engineers build and maintain the systems that collect, store, and process data at scale. In global tech companies, they design pipelines, optimise data warehouses, and ensure data quality for analytics and machine learning. Day to day involves writing code, tuning SQL, and collaborating with analysts and product teams.

Role and Technical Knowledge

1. 1

   Explain the difference between ETL and ELT. When would you choose one over the other?

   Highlight that ETL transforms before loading, while ELT loads raw data then transforms. Choose ELT for cloud warehouses with scalable compute, ETL for legacy systems with limited storage.

2. 2

   What are the key considerations when designing a data warehouse schema for analytics?

   Mention star vs snowflake schemas, denormalization for query performance, handling slowly changing dimensions, and partitioning for scalability.

3. 3

   How does Snowflake's architecture differ from traditional databases?

   Focus on separation of storage and compute, virtual warehouses, and automatic scaling. Avoid claiming Snowflake asks this; just explain the concept.

4. 4

   Describe how you would handle late-arriving data in a daily batch pipeline.

   Discuss using watermarks, reprocessing windows, and idempotent writes. Mention partitioning by event time and updating aggregates incrementally.

Practical Scenarios

1. 5

   A pipeline that loads data from Kafka to Snowflake is falling behind. How would you diagnose and fix it?

   Check consumer lag, Kafka partition count, Snowflake warehouse size, and network latency. Suggest increasing parallelism or resizing warehouse.

2. 6

   You need to migrate 10 TB of on-premises data to a cloud data warehouse with minimal downtime. Outline your approach.

   Propose using cloud storage as staging, parallel uploads, incremental syncs, and validation checks. Mention cutover strategy and rollback plan.

3. 7

   How would you ensure data quality in a pipeline that processes millions of records daily?

   Talk about automated tests, data profiling, anomaly detection, and alerting. Include checks for nulls, duplicates, and schema drift.

4. 8

   A business user reports that a dashboard shows incorrect numbers. How do you trace the issue?

   Start from the dashboard, trace back through transformations, check source data, and validate logic. Suggest adding lineage and monitoring.

Coding and SQL

1. 9

   Write a SQL query to find the second highest salary from an employee table.

   Use a subquery with MAX or window function DENSE_RANK. Explain handling ties and nulls.

2. 10

   Given a table of user events, write a query to calculate the daily active users for the last 30 days.

   Use COUNT(DISTINCT user_id) grouped by date, filtered by date range. Mention timezone considerations.

3. 11

   How would you optimise a slow-performing SQL query on a large table?

   Discuss indexing, partitioning, avoiding SELECT *, using EXPLAIN plans, and rewriting joins or subqueries.

4. 12

   Implement a Python function to read a CSV file, filter rows where a column value exceeds a threshold, and write the result to a new CSV.

   Use pandas for simplicity, handle exceptions, and mention memory efficiency for large files with chunking.

Behavioural and HR

1. 13

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

   Use STAR method, emphasize proactive learning and applying it to deliver results. Show curiosity and adaptability.

2. 14

   Describe a situation where you disagreed with a teammate on a technical approach. How did you resolve it?

   Focus on data-driven discussion, listening, and finding a compromise. Highlight outcome and improved collaboration.

3. 15

   Why do you want to work as a Data Engineer in a global tech company?

   Connect personal interest in data infrastructure with the company's scale and impact. Mention eagerness to solve complex data challenges.

4. 16

   How do you prioritize tasks when you have multiple deadlines?

   Explain using urgency and impact, communicating with stakeholders, and using tools like Jira. Give an example from academics or projects.