Simcorp India Data Engineer Interview: Rounds, Questions and Preparation

What the job involves

Data Engineers build and maintain the pipelines that move, clean, and store data so analysts and data scientists can use it. In global tech companies, they work with cloud services, SQL, Python, and big data tools to ensure data is reliable and timely. Day to day, they write code, monitor jobs, fix failures, and optimise performance.

Role and Technical Knowledge

1. 1

   What is the difference between ETL and ELT, and when would you use each?

   Explain that ETL transforms before loading, while ELT loads raw data first and transforms later. Mention that ELT is common with cloud warehouses due to scalability.

2. 2

   How would you handle a slow-running SQL query in a production pipeline?

   Talk about checking execution plans, adding indexes, avoiding SELECT *, and partitioning large tables. Emphasise monitoring and iterative testing.

3. 3

   Explain how you would design a data pipeline to ingest daily sales data from multiple sources.

   Cover source identification, scheduling (e.g., cron or Airflow), data validation, error handling, and storage in a warehouse. Mention idempotency and retries.

4. 4

   What are the key differences between a data lake and a data warehouse?

   Contrast schema-on-read vs schema-on-write, storage cost, and use cases. Give examples like S3 for lake and Redshift for warehouse.

Practical Scenarios

1. 5

   A pipeline fails at 2 AM and the next day's reports are missing. How do you respond?

   Describe checking logs, identifying root cause, restarting from the last successful point, and notifying stakeholders. Highlight preventing recurrence with alerts.

2. 6

   You need to migrate an on-premise data warehouse to the cloud. What steps would you take?

   Outline assessment, schema conversion, data transfer strategy, testing, and cutover plan. Mention minimising downtime and validating data consistency.

3. 7

   How would you ensure data quality in a pipeline that processes customer transactions?

   Discuss validation rules, deduplication, checksums, and monitoring dashboards. Include handling nulls and outliers.

4. 8

   You are asked to reduce cloud storage costs for a data lake. What approach would you take?

   Suggest lifecycle policies, compression, partitioning, and deleting unused data. Mention using cheaper storage tiers for old data.

Behavioural and HR

1. 9

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

   Use STAR format: describe the situation, the technology, how you learned it, and the outcome. Show initiative and results.

2. 10

   Describe a situation where you had to work with a team to solve a complex problem.

   Highlight your role, communication, and how you leveraged team strengths. End with a positive result and what you learned.

3. 11

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

   Connect your interest in data infrastructure to the company's scale and impact. Mention eagerness to work with large datasets and modern tools.

4. 12

   How do you handle tight deadlines or pressure?

   Give an example of prioritising tasks, staying calm, and communicating progress. Show that you focus on solutions, not stress.

Problem Solving and Coding

1. 13

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

   Use a subquery with MAX or ORDER BY with LIMIT/OFFSET. Explain handling duplicates and nulls.

2. 14

   Given a list of integers, write a Python function to return the sum of all even numbers.

   Show a clean function with a loop or list comprehension. Mention edge cases like empty list.

3. 15

   How would you find and remove duplicate records in a large dataset using Python?

   Discuss using pandas drop_duplicates or a set for memory efficiency. Mention sorting and hashing for large files.

4. 16

   Explain how you would debug a Python script that fails intermittently when reading from a database.

   Talk about logging, try-except blocks, connection pooling, and reproducing the issue. Suggest adding retries and timeouts.