**E:** Read source (CSV, API, DB). **T:** dropna, cast types, filter, derive columns, validate. **L:** to_sql, to_parquet. Add: logging, error handling, idempotency (overwrite by date), incremental (filter by watermark)....
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like Morgan Stanley. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl, python, sql) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
E: Read source (CSV, API, DB). T: dropna, cast types, filter, derive columns, validate. L: to_sql, to_parquet. Add: logging, error handling, idempotency (overwrite by date), incremental (filter by watermark).
# Extract
df = pd.read_csv(source) | pd.read_sql(query, conn)
# Transform
df = df.dropna(subset=['key']).astype(dtypes).query('amount>0')
df['derived'] = df['a'] + df['b']
# Load
df.to_sql('table', engine, if_exists='append', index=False)
# + validate schema, log row counts, handle duplicates
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.