Interview Practice — Data Engineering Flashcards

These flashcards cover key topics from an interview practice focused on data engineering, including technical skills, project experiences, and professional principles.

What is your professional background?

I'm a data and analytics professional with 7+ years at Arvig specializing in BigQuery, SQL, dbt, cloud pipelines, and analytics modeling.

Describe a cloud-based ingestion pipeline you built.

I built a serverless Cloud Run ingestion pipeline that downloads PRISM weather data, extracts GeoTIFFs, flattens raster data, and loads it into a partitioned BigQuery table.

What was your churn modeling project about?

I built a dbt pipeline with window functions and an advanced status engine to differentiate true churn from plan changes.

What did your SKU reduction pricing project involve?

I standardized SKUs, built a forecasting model, validated results, and supported pricing decisions affecting 20k+ accounts.

What analytics dashboard did you build?

I built the Arvig Mobile dashboard tracking growth, churn, promos, and product mix using BigQuery + Looker Studio.

What was a data challenge you faced?

Churn numbers didn’t reconcile due to double-counted churn; after auditing the logic, I corrected the formula and restored trust.

Why are you a good fit for this role?

I already build scalable pipelines, dbt models, governed datasets, and analytics used across the business.

How do you ensure data accuracy?

Through QC checks, reconciliation logic, peer review, and structured modeling layers.

What is incremental modeling in dbt?

Incremental models process only new data rather than rebuilding the entire table, reducing cost and improving efficiency.

How do you handle inconsistent data across systems?

By standardizing definitions, cleaning source data, building staging/core/mart layers, and validating assumptions with stakeholders.

How do you design a scalable data pipeline?

I break pipelines into ingestion, storage, transformation, and serving layers, use serverless components like Cloud Run, implement retries and logging, and push transformations into dbt.

Describe your experience with BigQuery partitioning and clustering.

I use date partitioning for performance and cost savings and apply clustering to frequently filtered columns like customer_id or variable.

How do you optimize SQL queries in BigQuery?

I avoid SELECT *, use partition filters, cluster-friendly predicates, minimize shuffles, and materialize intermediate steps when needed.

Can you describe a time you improved a process?

I redesigned the churn pipeline using incremental dbt models, which reduced full-table scans and significantly lowered compute cost.

What’s the difference between ETL and ELT?

ETL transforms data before loading it; ELT loads raw data first and transforms inside the warehouse, which is the BigQuery/dbt pattern.

How do you approach data validation?

I validate at ingestion, staging, and mart layers through bucketed counts, schema checks, null tests, and business logic QC.

What are dbt tests?

dbt provides schema tests like unique, notnull, acceptedvalues and custom tests to validate business logic.

Describe a time you had conflicting stakeholder requirements.

During SKU reduction, product wanted lower complexity, finance wanted revenue protection, and I built scenario models that satisfied both.

How do you manage technical debt in data models?

I refactor staging layers, break monolithic SQL, enforce naming conventions, and document sources and dependencies.

What’s your approach to KPI definition?

I align with stakeholders, document logic, validate edge cases, and build KPIs into dbt models to guarantee consistency.

How do you document data pipelines?

I use dbt documentation, README files, data dictionaries, and architecture diagrams.

Describe your experience with service accounts and IAM.

I set dataset-level roles, follow least-privilege principles, and separate ingestion, modeling, and reporting identities.

Explain a time you automated a manual process.

I automated PRISM ingestion using Cloud Run + Scheduler, eliminating manual downloads.

How do you handle slowly changing dimensions?

I use type 2 logic with effectivedate and enddate fields, or BigQuery merge statements depending on needs.

Describe a time you influenced a decision with data.

I produced pricing scenario models that drove the final 2024 pricing structure approved by leadership.

How do you measure pipeline reliability?

I monitor failures, latency, freshness, row counts, and test failures in dbt and GCP logs.

Explain your approach to data modeling.

I follow the layered approach: staging → core → marts, use clear naming, and modular SQL.

Describe your experience with Looker Studio.

I build interactive dashboards with filters, drilldowns, and BigQuery-backed datasets that refresh daily.

What is data governance to you?

Consistent definitions, standardized SKUs/fields, validation processes, documented logic, and controlled access.

Explain a time you learned something difficult.

Debugging churn logic taught me to question assumptions and validate formulas with raw data.

What’s your approach to debugging SQL?

Break queries into CTEs, inspect intermediate outputs, test edge cases, and cross-check against raw data.

Describe a high-pressure data situation.

During SKU reduction modeling, accuracy was critical because results impacted 20k+ accounts; I built extensive QC layers.

How do you ensure stakeholder alignment?

Frequent check-ins, demos, shared definitions, and validating logic early.

Explain your experience with Python for data engineering.

I use Python in Cloud Run for ingestion tasks, API retrieval, and file transformations like GeoTIFF extraction.

Describe a time you had to learn a new tool quickly.

I learned GeoTIFF raster flattening and Cloud Run deployment for the PRISM project.

What is your preferred warehouse design style?

Dimensional modeling with clear facts, dimensions, and thin staging layers.

Explain BigQuery cost optimization.

Use partitioning, clustering, incremental models, avoid SELECT *, and limit processed bytes.

Describe a project where accuracy was essential.

SKU reduction required perfect accuracy because it would update 20k+ accounts; I implemented strict QC and validation.

What motivates you as a data engineer?

Building clean, reliable systems that empower the business and reduce friction for analysts.

Where do you want to grow next?

Cloud architecture, Data Fusion pipelines, and deeper workflow orchestration in GCP.