About DataEngPrep
The interview prep platform built by data engineers, for data engineers.
Our Mission
Data engineering interviews are uniquely difficult. They span SQL, distributed systems, cloud architecture, pipeline design, and behavioral questions — yet most preparation resources treat data engineering as an afterthought to software engineering.
DataEngPrep exists to change that. We provide a dedicated, AI-powered platform that helps data engineers at every level — from entry-level analysts transitioning into DE roles to senior engineers preparing for Staff+ positions at FAANG companies — practice with real interview questions and receive expert-quality feedback instantly.
What We Offer
- 1,863+ Real Interview Questions — sourced from actual data engineering interviews at 98+ companies including Amazon, Google, Databricks, Snowflake, Meta, Netflix, and Microsoft. Every question includes a detailed, expert-written answer and approach guidance.
- AI Mock Interview Coach — our 3-agent AI system simulates a real interview panel: one agent plays a FAANG-caliber interviewer, another evaluates your answers against hiring committee standards, and a third acts as a Staff Engineer mentor who explains concepts in depth.
- ATS Resume Analyzer — upload your resume for instant, actionable feedback on keyword optimization, formatting, and alignment with data engineering job descriptions.
- SQL Playground — practice SQL queries in a live, browser-based environment with real datasets.
- Company-Specific Prep — filter questions by company, difficulty, and topic to build a targeted study plan.
Editorial Standards
Quality is our top priority. Every question and answer on DataEngPrep goes through a rigorous multi-step editorial process:
- Sourcing — questions are collected from verified interview experiences, community contributions, and hiring manager input.
- Expert Review — each answer is written or reviewed by practicing data engineers with hands-on experience in production systems, distributed computing, and cloud platforms.
- Categorization & Tagging — questions are categorized by topic (SQL, Spark, Python, System Design, Cloud, Behavioral), difficulty level, and originating company for effective study planning.
- Continuous Updates — our question bank is regularly updated to reflect current industry trends, new tools, and evolving interview patterns.
Who We Are
DataEngPrep is run by a small editorial team of practising data engineers who have collectively designed and operated production data platforms across batch ETL, streaming, and analytical workloads. Our day jobs involve the same technologies our questions cover: Apache Spark, Snowflake, Databricks, Kafka, Airflow, BigQuery, dbt, and the cloud platforms (AWS, GCP, Azure) that host them.
We started DataEngPrep because the data engineering interview prep landscape is fragmented — generic LeetCode problems, scattered Medium articles, and outdated PDF question banks that don’t reflect what a modern data engineering interview actually asks. Our goal is to consolidate the real question patterns we and our peers have seen into one searchable, systematically-reviewed reference that engineers at any level can use.
Meet the Editor
Practising data engineer and the lead reviewer for the DataEngPrep question bank. Aditya personally reviews and signs off on the SQL, Spark, and System Design answers, drafts the editorial standards the rest of the team follows, and is the address corrections land at.
Editorial Credentials
- Combined hands-on experience designing and shipping data platforms in production at consulting, startup, and enterprise environments.
- Direct interview experience on both sides of the table — as candidates interviewing at top tech companies and as engineers conducting technical rounds for new hires.
- Working knowledge of the specific systems each question covers (rather than surface-level summaries from documentation), so answers reflect what actually breaks in production.
How We Vet Questions
Every question in our database goes through a four-step review:
- Source verification — we only include questions that have been reported in real interviews (from candidates who interviewed within the last 24 months) or that test concepts we’ve personally been asked.
- AI-assisted drafting — initial answers are generated with a tuned model, then handed to a human reviewer.
- Human technical review — a practising data engineer edits the answer for technical accuracy, removes plausible-but-wrong claims, and adds production context (gotchas, trade-offs, scaling considerations).
- Continuous correction — when readers email us about an inaccuracy, we update the answer and the page’s last-modified date. Email info@dataengprep.tech if you spot something wrong.
See our full AI Disclosure & Editorial Policy for what AI does and doesn’t do in our content pipeline.
Our Technology
DataEngPrep is built on a modern tech stack designed for performance and reliability:
- AI Engine — powered by Google Gemini 2.5 Flash with custom-tuned system prompts, structured JSON output, and a 3-agent orchestration architecture for realistic interview simulation.
- Frontend — Next.js App Router with server-side rendering for fast load times and SEO.
- Authentication & Data — Firebase Auth and Firestore for secure user management and real-time usage tracking.
Transparency & AI Use
We use AI (Google Gemini) to power the Answer Analyzer, Mock Interview, and Resume Optimizer features. Long-form blog content is drafted with AI assistance and then human-edited by practising data engineers for technical accuracy and current relevance. We do not invent named user testimonials, do not present AI output as personal experience, and surface a "Last updated" date on every blog article. Read our full AI Disclosure & Editorial Policy for the complete breakdown.
Get in Touch
We'd love to hear from you. Whether you have feedback, partnership proposals, or just want to say hi:
- Email: info@dataengprep.tech
- Visit our Contact page