BeyondTrust India Data Engineer Interview: Rounds, Questions and Preparation

What the job involves

Data Engineers build and maintain data pipelines that move and transform data from source systems to storage and analytics platforms. In a global tech product company, you will work with cloud services, SQL, and distributed processing tools to ensure reliable, timely data for analytics and machine learning. Day to day tasks include writing ETL code, monitoring pipelines, and optimising data storage for performance and cost.

Role and Technical Knowledge

1. 1

   Explain the difference between ETL and ELT. When would you use each in a cloud-based product company?

   Strong answer covers extraction, transformation, loading order, and how cloud warehouses like Snowflake or BigQuery enable ELT by transforming after loading. Mention cost and scalability trade-offs.

2. 2

   What is a star schema and why is it commonly used in data warehousing for analytics?

   Describe fact and dimension tables, denormalisation benefits for query performance, and simplicity for business intelligence tools. Contrast with snowflake schema.

3. 3

   How would you handle slowly changing dimensions (SCD) Type 2 in a data pipeline?

   Explain tracking historical changes by adding new rows with effective dates and current flag. Mention surrogate keys and how to update dimension tables without losing history.

4. 4

   Write a SQL query to find the second highest salary from an Employee table. Explain your approach.

   Use a subquery with MAX or a window function like DENSE_RANK. Discuss handling duplicates and NULLs, and why window functions are often preferred.

Practical Scenarios and Problem Solving

1. 5

   A daily data pipeline that loads customer data into a warehouse has been failing for the last three days. How would you debug and fix it?

   Outline checking logs, source system availability, schema changes, and permissions. Mention steps to rerun failed jobs and add monitoring alerts to prevent recurrence.

2. 6

   You need to process a 100 GB log file to extract daily active users. How would you approach this with limited memory?

   Discuss streaming or chunked processing, using tools like Spark or Python with chunked reads, and aggregating counts. Mention partitioning and avoiding loading entire file into memory.

3. 7

   How would you design a data pipeline to ingest data from multiple REST APIs that have rate limits and occasional downtime?

   Cover retry logic with exponential backoff, scheduling, storing raw data first, and idempotent writes. Mention using a workflow orchestrator like Airflow.

4. 8

   Your manager asks you to reduce the cost of a Spark job that runs daily. What steps would you take?

   Suggest optimising partitioning, using columnar formats like Parquet, caching intermediate results, and tuning executor memory. Mention monitoring job metrics to identify bottlenecks.

Behavioural and HR Questions

1. 9

   Tell me about a time you had to learn a new technology quickly for a project. How did you approach it?

   Use STAR method: describe the situation, the technology, your learning strategy (documentation, tutorials, practice), and the outcome. Show curiosity and self-drive.

2. 10

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

   Focus on listening, data-driven discussion, and compromise. Highlight respect for others' views and willingness to prototype or test both ideas.

3. 11

   Why do you want to work as a Data Engineer in a global product company rather than a services company?

   Show interest in building scalable systems, working with large datasets, and owning pipelines end-to-end. Mention desire to learn from product-focused engineering culture.

4. 12

   Where do you see yourself in three years as a Data Engineer?

   Express growth in technical depth (e.g., distributed systems, cloud certifications) and taking ownership of critical data infrastructure. Align with company's need for reliable data platforms.

Coding and SQL Practice

1. 13

   Given a table of user events with timestamps, write a SQL query to find users who logged in on consecutive days.

   Use window functions like LAG or LEAD to compare dates, or self-join on date difference. Explain handling of time zones and duplicate events.

2. 14

   Write a Python function to read a CSV file and return the top 3 products by total sales amount.

   Use pandas groupby and sort. Discuss handling missing values, data types, and memory efficiency for large files.

3. 15

   How would you remove duplicates from a large dataset using SQL and Python?

   In SQL, use ROW_NUMBER() with PARTITION BY and filter. In Python, use drop_duplicates after sorting. Mention deciding which duplicate to keep based on business rules.

4. 16

   Explain how you would implement a simple data validation check in an ETL pipeline.

   Describe checking row counts, nulls, data types, and referential integrity. Mention logging failures and quarantining bad records for review.