Informatica India Data Engineer Interview: Rounds, Questions and Preparation

What the job involves

Data Engineers build and maintain the systems that collect, store, process, and transform raw data into clean, usable datasets for analytics and machine learning. In global tech companies, they work with cloud platforms, big data tools, and ETL pipelines to ensure data flows reliably from source systems to data warehouses and lakes. Day to day, they write code, design data models, troubleshoot pipeline failures, and collaborate with analysts and data scientists.

Role and Technical Knowledge

1. 1

   What is the difference between a data warehouse and a data lake?

   Explain that a warehouse stores structured, processed data for analytics, while a lake stores raw data in any format. Mention use cases and how both can coexist in a modern data architecture.

2. 2

   Explain ETL vs ELT and when you would use each.

   Define Extract, Transform, Load and Extract, Load, Transform. Discuss how cloud warehouses make ELT popular because of scalable compute, and give examples of when transformation is better before or after loading.

3. 3

   What are the different types of joins in SQL and how do they affect result sets?

   Cover inner, left, right, full outer, and cross joins. Explain with a small example how each returns rows, and mention that null handling and duplicate keys can change results.

4. 4

   How would you design a schema for a retail sales data warehouse?

   Talk about star schema with fact and dimension tables. Identify facts like sales amount and dimensions like product, customer, time. Mention slowly changing dimensions and why denormalization helps query performance.

Practical Scenarios

1. 5

   A daily ETL job that loads sales data is failing intermittently. How would you debug it?

   Start by checking logs for errors, then verify source data availability, network issues, and resource limits. Suggest adding retries, alerts, and data quality checks to prevent future failures.

2. 6

   You need to process 1 TB of log data daily and make it queryable within an hour. What approach would you take?

   Propose a distributed processing framework like Spark, store raw data in a data lake, and use partitioning and columnar formats like Parquet. Mention orchestration with Airflow and cost considerations.

3. 7

   How would you handle duplicate records in a data pipeline?

   Explain deduplication using unique keys, window functions like ROW_NUMBER, or merge statements. Discuss idempotent pipelines and how to decide which record to keep based on timestamps or source priority.

4. 8

   Your manager asks you to reduce the cost of a cloud-based data pipeline. What steps would you take?

   Suggest right-sizing clusters, using spot instances, compressing data, partitioning tables, and scheduling jobs during off-peak hours. Mention monitoring and setting budget alerts.

Coding and Data Structures

1. 9

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

   Use a subquery with MAX and a WHERE clause, or use DENSE_RANK. Explain the difference between RANK and DENSE_RANK for ties.

2. 10

   Given a list of transactions, write a Python function to find the top 3 customers by total purchase amount.

   Use a dictionary to aggregate amounts, then sort by value. Discuss time complexity and how you would handle ties or missing data.

3. 11

   How would you find duplicate rows in a table using SQL?

   Use GROUP BY with HAVING COUNT(*) > 1 on the relevant columns. Mention that you can also use window functions to identify duplicates without grouping.

4. 12

   Explain how you would reverse a linked list and why it might be asked in a data engineering interview.

   Describe iterative and recursive approaches. Connect it to understanding pointers and memory, which helps in optimizing data processing code and understanding data structures used in databases.

Behavioural and HR

1. 13

   Tell me about a time you worked in a team to complete a project under a tight deadline.

   Use the STAR method. Focus on your specific contribution, how you communicated, and the outcome. Highlight learning from any challenges.

2. 14

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

   Show passion for data and large-scale systems. Mention the company's data-driven culture and how your skills align. Avoid generic answers about 'good company'.

3. 15

   Describe a situation where you had to learn a new technology quickly for a project.

   Give a concrete example, such as learning a cloud service or a new framework. Explain your learning approach and how you applied it successfully.

4. 16

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

   Show ambition to grow into a senior or lead role, deepening expertise in data architecture or cloud technologies. Align your goals with the company's growth.