Data Engineer Technical Interview Preparation

Introduction emphasizes that data engineer interviews focus on strings, lists, and dictionaries for data manipulation, storage, retrieval, and aggregation, with basic Big O concepts and practice of SQL Leetcode questions.

Prepare for SQL and Python coding interviews with a dataset of three to five tables, mastering group by, having, aggregates, window functions such as row_number, rank, dense_rank, and joins.

Learn to manipulate strings, lists, and dictionaries in Python, count occurrences with a dict counter, reverse strings, extract distinct letters, and split strings into lists while considering time complexity.

Explore list processing by counting occurrences and extracting distinct values using a dictionary counter. Tackle nested lists with recursion, avoid nested loops, and compare append versus extend.

Store and retrieve data across related dictionaries, handling ETL rounds with streaming and batch data. Store aggregated metrics, such as cumulative sum and event count, to compute averages quickly.

Explore product sense through case-study scenarios and metrics that drive data-driven decisions, focusing on users, usage, and conversions within a clear framework for measuring success, diagnosing problems, or launch decisions.

Confirm the product's purpose, users, and market position with trends, then choose a goal and metric (active users, interactions, or conversions), plus an X percent target and a/b testing plan.

Clarify how the x percent drop is measured, segment the user base, and test a single theory with targeted metrics to diagnose and fix metric changes.

Define metrics and a guardrail metric to decide a product launch, identify target groups for A/B testing, and assess short- and long-term impact on users, engagement, and monetization.

Model data from a business scenario, justify scalable, maintainable designs, and write queries based on your tables. Leverage a star schema to support common query patterns and performance.

Define the star schema by detailing the fact table with its grain and primary key, refreshing via upserts, then add 3-5 dimensions with primary keys and foreign keys, plus fields.

Stay calm and think critically, narrating your approach aloud to guide the interviewer. Check in often, seek feedback, and practice with mock interviews to stay persistent.

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