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Technical Phone Screen — Data Engineer

Screen for data engineering: SQL proficiency, pipeline design, and data modeling.

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Evaluation Criteria

SQL Proficiency & Query Design

Evaluates depth of SQL knowledge including window functions, CTEs, and query optimization.

Sample Questions

Explain the difference between a window function and a GROUP BY — when do you use each?
How would you find the top 3 products by revenue per category in a single query?
What strategies do you use to optimize slow-running queries on large datasets?

Strong Signal

Candidate writes or describes complex queries fluently, uses window functions and CTEs naturally, and explains query execution plans. They discuss partitioning and indexing strategies for analytical workloads.

Weak Signal

Candidate struggles with basic JOINs, cannot explain when to use window functions, or has only worked with simple SELECT statements.

Data Pipeline Design & ETL/ELT

Assesses experience designing and maintaining reliable data pipelines at scale.

Sample Questions

Walk me through how you would design a pipeline to ingest data from an external API into a data warehouse.
What's the difference between ETL and ELT, and when would you choose each?
How do you handle schema changes in upstream data sources?

Strong Signal

Candidate describes orchestration tools (Airflow, Dagster, Prefect), discusses idempotency, retry logic, and backfill strategies. They consider data freshness requirements and SLAs.

Weak Signal

Candidate has only written standalone scripts with no orchestration, doesn't consider failure modes, or cannot explain how they ensure pipeline reliability.

Data Modeling & Warehouse Design

Evaluates understanding of dimensional modeling, star/snowflake schemas, and warehouse architecture.

Sample Questions

How do you decide between a star schema and a snowflake schema?
Explain slowly changing dimensions — which types have you used and why?
How do you model a many-to-many relationship in a dimensional model?

Strong Signal

Candidate explains fact/dimension tables clearly, discusses trade-offs of different SCD types, and considers query patterns when designing models. They mention tools like dbt for transformation layers.

Weak Signal

Candidate has no exposure to dimensional modeling, conflates OLTP and OLAP design, or cannot explain why warehouse design differs from application database design.

Data Quality & Testing

Assesses the candidate's approach to ensuring data reliability and catching issues proactively.

Sample Questions

How do you ensure the quality of data flowing through your pipelines?
What data testing frameworks or approaches have you used?
Describe a time you caught a data quality issue before it reached stakeholders.

Strong Signal

Candidate describes data contracts, schema validation, row count checks, freshness monitoring, and anomaly detection. They mention tools like Great Expectations, dbt tests, or Monte Carlo.

Weak Signal

Candidate does no data validation, waits for downstream users to report issues, or assumes source data is always correct.

Cloud & Big Data Technologies

Evaluates hands-on experience with cloud data platforms and big data processing frameworks.

Sample Questions

What cloud data platforms have you worked with, and how did you choose between them?
When would you use Spark vs. a simpler approach like SQL on a warehouse?
How do you manage costs in a cloud data environment?

Strong Signal

Candidate has hands-on experience with platforms like Snowflake, BigQuery, Redshift, or Databricks. They can reason about when distributed processing is needed vs. overkill and discuss cost optimization.

Weak Signal

Candidate lists tools they've only read about, cannot explain when big data tools are actually necessary, or has no awareness of cost implications.

Red Flags to Watch For

Cannot write a basic SQL JOIN or explain how GROUP BY works
Has never considered data quality or pipeline reliability
Claims Spark experience but cannot explain what a partition is or when distributed processing is needed