Explain why you want to pursue data analysis, what kinds of data problems excite you, and how you use data to influence decisions. Describe relevant projects, tools, and techniques you have used such as data cleaning, exploratory analysis, visualization, or basic statistical inference, and provide examples of insights you generated and their business impact. Discuss domain interests, ability to communicate findings to nontechnical stakeholders, and how the role aligns with your learning goals and career path. For entry level candidates include coursework, competitions, or personal projects that demonstrate curiosity with data.
EasyTechnical
96 practiced
Which industry domains interest you most (e.g., fintech, healthcare, retail)? For one domain you pick, explain two unique data challenges (privacy, sparsity, event volume, seasonality) and how domain knowledge would change your data engineering decisions around schema design, privacy controls, and latency requirements.
HardTechnical
141 practiced
A senior stakeholder asks you to publish a simple metric (e.g., conversion rate that includes canceled orders) that you know will be misleading to drive a presentational win. How would you handle the request ethically while maintaining trust? Provide the conversation steps, alternative metric recommendations, and how you would monitor downstream usage of the requested metric.
MediumTechnical
95 practiced
Given a new 'user_events' table with columns (user_id, event_type, event_timestamp, metadata JSON), outline an exploratory data analysis (EDA) plan to discover useful aggregations and detect anomalies. List SQL queries, summary statistics, visualizations, cardinality checks, and integrity tests you would run in your first analysis pass.
HardSystem Design
106 practiced
Plan a migration of analytics workloads from an on-prem Hadoop cluster to a cloud data warehouse (e.g., BigQuery or Snowflake). Include discovery and inventory steps, strategy for data transfer and job migration, schema and query validation approach, maintaining performance parity, cost estimation, cutover plan with rollback, and communication with analytics consumers.
MediumTechnical
98 practiced
You inherit a daily metrics aggregation pipeline for user engagement where stakeholders report intermittent 10–15% discrepancies between the BI dashboard and raw event counts. Walk through your investigation plan: what logs and checks will you run, how will you isolate root causes (schema changes, joins, timezone handling, deduplication, late events), and what short- and long-term fixes would you propose.
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