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A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

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Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

In an A/B test, how can you check if assignment to the various buckets was truly random?

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

In an A/B test, which of the following is a common method to determine if the assignment to the test groups was truly random?

Random Bucketing

A/B Testing & Experimentation

Blend Salaries by Position

We are looking for an experienced Data Engineering Manager with a strong foundation in Python, SQL, and Spark, and hands-on expertise in AWS, Databricks. In this role, you will build and maintain scalable data pipelines and architecture to support analytics, data science, and business intelligence initiatives. You’ll work closely with cross-functional teams to drive data reliability, quality, and performance.Responsibilities:<ul><li>Design, develop, and optimize scalable data pipelines using Databricks in AWS such as Glue, S3, Lambda, EMR, Databricks notebooks, workflows and jobs.</li><li>Building data lake in WS Databricks.</li><li>Build and maintain robust ETL/ELT workflows using Python and SQL to handle structured and semi-structured data.</li><li>Develop distributed data processing solutions using Apache Spark or PySpark.</li><li>Partner with data scientists and analysts to provide high-quality, accessible, and well-structured data.</li><li>Ensure data quality, governance, security, and compliance across pipelines and data stores.</li><li>Monitor, troubleshoot, and improve the performance of data systems and pipelines.</li><li>Participate in code reviews and help establish engineering best practices.</li><li>Mentor junior data engineers and support their technical development.</li></ul> Requirements:<ul><li>Bachelor's or master's degree in computer science, Engineering, or a related field.</li><li>8+ years of hands-on experience in data engineering, with at least 3 years working with AWS Databricks.</li><li>Strong programming skills in Python for data processing and automation.</li><li>Advanced proficiency in SQL for querying and transforming large datasets.</li><li>Deep experience with Apache Spark/PySpark in a distributed computing environment.</li><li>Solid understanding of data modelling, warehousing, and performance optimization techniques.</li><li>Proficiency with AWS services such as Glue, S3, Lambda and EMR.</li><li>Experience with version control Git or Code commit</li><li>Experience in any workflow orchestration like Airflow, AWS Step functions is a plus.</li><li>Excellent communication and stakeholder management skills with a strategic mindset.</li></ul>

We’re looking for a skilled Senior Data Engineers/Data Engineers with strong experience in distributed systems, Python/Scala, and modern data engineering tools to help design and implement an end-to-end data architecture for a leading enterprise client. This role is part of a strategic initiative to enable robust analytics, BI, and operational workflows across both on-premise and cloud environments.In this role, you’ll work closely with both Blend’s internal teams and client stakeholders to build and optimize data pipelines, support data modeling efforts, and ensure reliable data flows for analytics, reporting, and business decision-making.This position is ideal for engineers with a strong data foundation who are looking to apply their skills in large-scale, modern data environments, while gaining exposure to advanced architectures and tooling.You will:<ul><li>Design and implement an end-to-end data solution architecture tailored to enterprise analytics and operational needs.</li><li>Build, maintain, and optimize data pipelines and transformations using Python, SQL, and Spark.</li><li>Manage large-scale data storage and processing with Iceberg, Hadoop, and HDFS.</li><li>Develop and maintain dbt models to ensure clean, reliable, and well-structured data.</li><li>Implement robust data ingestion processes, integrating with third-party APIs and on-premise systems.</li><li>Collaborate with cross-functional teams to align on business goals and technical requirements.</li><li>Contribute to documentation and continuously improve engineering and data quality processes.</li></ul>Qualifications<ul><li>2+ years of experience in data engineering, including at least 1 year with on-premise systems.</li><li>Proficiency in Python for data workflows and pipeline development.</li><li>Strong experience with SQL, Spark, Iceberg, Hadoop, HDFS, and dbt.</li><li>Familiarity with third-party APIs and data ingestion processes.</li><li>Excellent communication skills, with the ability to work independently and engage effectively with both technical and non-technical stakeholders.</li></ul>Preferred Qualifications<ul><li>Master’s degree in Data Science or a related field.</li><li>Experience with Airflow or other orchestration tools.</li><li>Hands-on experience with Cursor, Copilot, or similar AI-powered developer tools.</li></ul>

Question	Topic	Difficulty	Ask Chance
Boarding Times Bias	Statistics	Medium	Very High
Hundreds of Hypotheses	Statistics	Medium	Very High
Random Bucketing	A/B Testing & Experimentation	Medium	Very High

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