How difficult are Meta Data Manipulation (SQL/Python) interview questions?

Meta Data Manipulation (SQL/Python) questions are typically medium-to-hard in difficulty and reward clarity under time pressure. Interviewers expect correct, efficient solutions that handle real-world data quirks such as NULLs, duplicates, and timezone or type issues. SQL problems often require joins, window functions, aggregations, and readable CTE structure rather than clever one-liners. Python tasks evaluate clean data transformations, appropriate use of lists/dictionaries or pandas, and algorithmic thinking for performance-sensitive steps. Strong candidates write defensible, testable code, explain tradeoffs, and can optimize a correct solution when prompted.

Where in the Meta interview process does Data Manipulation (SQL/Python) appear and what is the format?

Data manipulation shows up across screening and on-site (or virtual loop) stages, often during the technical screen and again in role-specific interviews for analytics, data engineering, or data scientist positions. Early screens commonly include a short set of timed problems split between SQL and Python executed in a shared editor. Later loop interviews expand scope with longer, end-to-end tasks that mix data modeling, metric definition, and manipulation tasks, and interviewers may probe for performance, edge cases, and how the candidate would productionize the transformation pipeline.

What is a realistic prep timeline for Meta Data Manipulation (SQL/Python) interviews?

A realistic prep timeline is four to eight weeks for focused preparation, although experienced practitioners may need less. Begin by refreshing SQL fundamentals and Python data structures, then practice common transformation problems and windowing scenarios. Midway through, introduce timed practice sessions mirroring the screening format to build speed and articulation. In the final weeks, do full mock interviews that combine SQL and Python work, review feedback, and polish explanations and test cases. Consistent, deliberate practice with real schema examples and post-solution optimizations produces the best results.

What key subtopics should I study for Data Manipulation (SQL/Python) at Meta?

Key SQL subtopics include joins (inner, left, semi/anti), aggregations, GROUP BY versus HAVING semantics, window functions, CTEs and subqueries, NULL handling, and basic performance considerations such as limiting data scanned and understanding indexes or partitioning. For Python, focus on core data structures, complexity reasoning, defensive coding for edge cases, idiomatic data transformations, and familiarity with pandas or equivalent libraries if appropriate. Also practice writing concise tests, handling date/time and string parsing, and explaining tradeoffs between readability and micro-optimizations.

What standout tips and common pitfalls should I keep in mind when preparing?

Standout tips include always clarifying assumptions up front, sketching the approach before coding, and using readable CTEs or helper functions to break complex logic into verifiable steps. Demonstrate awareness of edge cases, write simple test examples, and explain time/space complexity and production implications. Common pitfalls are ignoring NULL semantics, failing to deduplicate when required, overcomplicating queries instead of prioritizing clarity, and not defending performance tradeoffs. Finally, don’t forget to communicate continuously during the interview; interviewers evaluate reasoning and decision-making as much as the final result.

Meta Data Manipulation (SQL/Python) Questions (Updated 2026) — Page 9

Meta Data Manipulation (SQL/Python) Interview Questions

Meta Data Manipulation (SQL/Python) interview questions are a central part of Meta’s hiring for data scientist, data engineer, and analytics roles and usually emphasize practical, product-focused problem solving over abstract algorithm puzzles. What’s distinctive is the scale and product context: interview problems mirror real-world analytics tasks with messy data, session/event tables, and metrics design. Interviewers evaluate accuracy, clarity, and maintainability of your SQL or pandas code, your handling of edge cases (NULLs, deduplication, sampling), and your ability to explain trade-offs between readability and performance using CTEs, window functions, joins, and vectorized Python operations. For interview preparation, expect a timed technical screen (often using a shared editor) with SQL and Python data-manipulation tasks, followed by deeper loop rounds combining coding, product-metrics reasoning, and behavioral questions. Practice end-to-end problems: translate a product question into concrete metrics, write and optimize queries or pandas pipelines, narrate assumptions, and validate results. Work timed problems in CoderPad-like environments, rehearse clarifying questions, and review common pitfalls such as filter vs HAVING, NULL behavior, and inefficient joins. Regular mock interviews and focused drills on window functions, groupings, merges, and missing-data strategies will give the confidence and fluency Meta typically looks for.

Questions

169

Company

Updated

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169 Questions 1 Company

PLTCHK

"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"

_The_TaNk_

"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"

ChrisSenior SWE, LinkedIn

"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."

sleepy33

"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."

JakeSenior ML Engineer, Lyft

"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."

nuggetlord

"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."

CarlosFull Stack, Shopify

"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."

boba.tea.vibes

"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."

AndySWE-II, Google

"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."

couchpotato99

"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."

ShrutiData Engineer, Salesforce

"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."

midnightramen

"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."

BiancaFrontend Eng, Figma

"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."

tambrahm007

"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."

toa

"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."

PLTCHK

_The_TaNk_

ChrisSenior SWE, LinkedIn

"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."

sleepy33

"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."

JakeSenior ML Engineer, Lyft

"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."

nuggetlord

CarlosFull Stack, Shopify

"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."

boba.tea.vibes

"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."

AndySWE-II, Google

"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."

couchpotato99

"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."

ShrutiData Engineer, Salesforce

"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."

midnightramen

"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."

BiancaFrontend Eng, Figma

"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."

tambrahm007

"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."

toa

"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."

Showing 9 results

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Data Manipulation (SQL/Python)

Meta Data Manipulation (SQL/Python) Interview Questions

Identify Unique Callers and French Customer Call Percentage

Calculate Average Session Length and Compare App Performance

Calculate Response Rate and Compare User Survey Ratings

Calculate Weekly Thread Engagement with Reactions in SQL

Identify Top 10 Users by Average Call Duration

Determine Product Buyer Count and Interaction Percentage

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Analyze User Engagement Metrics for Video-Calling App

Frequently Asked Questions