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Data Engineering interviews are no longer about definitions or SQL puzzles. Companies want engineers who can think like owners, handle uncertainty, and solve messy real-world problems. That’s why scenario-based questions dominate every major tech interview today — from fintech to SaaS to product companies. Below is a breakdown of twelve core scenarios that expose whether […]

Below are the 20 most commonly asked Snowflake interview topics — explained clearly and practically. 1. What is Snowflake’s architecture and why is it unique? Snowflake uses a multi-cluster shared data architecture, separating compute, storage, and cloud services. 2. Explain Virtual Warehouses and how scaling works. A Virtual Warehouse is compute used for query processing.Supports: […]

Databricks interviews are brutally simple: either you understand the core building blocks, or you don’t. And the gap shows immediately. These 20 questions represent what actually matters — the logic behind Delta Lake, pipeline reliability, ingestion patterns, and performance. Let’s break them down with clarity and precision. 1. Delta Lake vs Parquet A Delta table […]

In most data engineering setups, teams rely on scheduled batch jobs that reload entire datasets daily — even if only 1% of the files have changed. That’s inefficient, slow, and expensive. Databricks Auto Loader solves this with one key idea: incremental ingestion. It continuously tracks and processes only new files, enabling near real-time ingestion without […]

Recently one of my friend went through the CGI Data Engineer interview process, and trust me — it was a mix of real-world data problems, PySpark logic, and Azure ecosystem understanding.If you’re preparing for Azure + Databricks roles, these questions will hit the exact level you’ll face. Role : AZURE Data EngineerCTC : 25 LPAExp […]

Role : Data EngineerCTC : 25 LPAExp : 5+ yearsDifficulty Level : MEDIUM1️⃣ PySpark: You have a dataset with user_id, timestamp, and transaction_amount.Write PySpark code to calculate each user’s average transaction in the last 30 days using window functions.2️⃣ SQL: Write a query to find the second highest salary in each department and handle cases […]

IBM Data Engineer interviews are no joke — they go deep into data architecture, AWS cloud pipelines, SQL performance tuning, and PySpark optimization. If you’re already experienced in AWS, Snowflake, or modern ETL frameworks, these 20 questions (and their detailed answers) will help you bridge your AWS expertise with IBM’s enterprise data expectations. Let’s dive […]

Below are 20 PySpark interview questions that appear in almost every data engineering interview — along with concise, practical answers you can use to demonstrate mastery. 1. Difference Between RDD, DataFrame, and Dataset 👉 In Python, we mostly use DataFrames. 2. Lazy Evaluation Spark doesn’t execute transformations immediately. It builds a logical DAG (Directed Acyclic […]

In the modern data ecosystem, automation is key to building reliable, efficient, and scalable data pipelines. As data volumes and frequency of updates increase, manually managing ETL (Extract, Transform, Load) jobs becomes inefficient. That’s where Snowflake Streams and Tasks come in — offering a simple yet powerful way to handle Change Data Capture (CDC) and […]

👉 Use staging tables and commit data only after full batch success. For Spark streaming, rely on checkpointing. Idempotent operations like MERGE ensure no duplicates. 👉 Idempotency means re-running the pipeline multiple times gives the same result. Important to avoid duplicates after failure/restart. Implemented using UPSERT/MERGE, unique keys, or partition overwrite. 👉 Track batch metadata […]