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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?

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

Decreasing Comments

How would you investigate a decline in the average number of comments per user?

Must be local to TX Data Engineer – SQL, Python and Pyspark Expert (Onsite – Dallas, TX) Data Engineer with strong proficiency in SQL, Python, and PySpark to support high-performance data pipelines and analytics initiatives. This role will focus on scalable data processing, transformation, and integration efforts that enable business insights, regulatory compliance, and operational efficiency. Key ResponsibilitiesDesign, develop, and optimize ETL/ELT pipelines using SQL, Python, and PySpark for large-scale data environmentsImplement scalable data processing workflows in distributed data platforms (e.g., Hadoop, Databricks, or Spark environments)Partner with business stakeholders to understand and model mortgage lifecycle data (origination, underwriting, servicing, foreclosure, etc.)Create and maintain data marts, views, and reusable data components to support downstream reporting and analyticsEnsure data quality, consistency, security, and lineage across all stages of data processingAssist in data migration and modernization efforts to cloud-based data warehouses (e.g., Snowflake, Azure Synapse, GCP BigQuery)Document data flows, logic, and transformation rulesTroubleshoot performance and quality issues in batch and real-time pipelinesSupport compliance-related reporting (e.g., HMDA, CFPB) Required Qualifications6+ years of experience in data engineering or data developmentAdvanced expertise in SQL (joins, CTEs, optimization, partitioning, etc.)Strong hands-on skills in Python for scripting, data wrangling, and automationProficient in PySpark for building distributed data pipelines and processing large volumes of structured/unstructured dataExperience working with mortgage banking data sets and domain knowledge is highly preferredStrong understanding of data modeling (dimensional, normalized, star schema)Experience with cloud-based platforms (e.g., Azure Databricks, AWS EMR, GCP Dataproc)Familiarity with ETL tools, orchestration frameworks (e.g., Airflow, ADF, dbt)

Job Title: Data Engineer(python, pyspark, databricks)Location: Dallas, TX Data Engineer with strong proficiency in SQL, Python, and PySpark to support high-performance data pipelines and analytics initiatives. This role will focus on scalable data processing, transformation, and integration efforts that enable business insights, regulatory compliance, and operational efficiency.Data Engineer – SQL, Python and Pyspark Expert (Onsite – Dallas, TX)Key ResponsibilitiesDesign, develop, and optimize ETL/ELT pipelines using SQL, Python, and PySpark for large-scale data environmentsImplement scalable data processing workflows in distributed data platforms (e.g., Hadoop, Databricks, or Spark environments)Partner with business stakeholders to understand and model mortgage lifecycle data (origination, underwriting, servicing, foreclosure, etc.)Create and maintain data marts, views, and reusable data components to support downstream reporting and analyticsEnsure data quality, consistency, security, and lineage across all stages of data processingAssist in data migration and modernization efforts to cloud-based data warehouses (e.g., Snowflake, Azure Synapse, GCP BigQuery)Document data flows, logic, and transformation rulesTroubleshoot performance and quality issues in batch and real-time pipelinesSupport compliance-related reporting (e.g., HMDA, CFPB)Required Qualifications6+ years of experience in data engineering or data developmentAdvanced expertise in SQL (joins, CTEs, optimization, partitioning, etc.)Strong hands-on skills in Python for scripting, data wrangling, and automationProficient in PySpark for building distributed data pipelines and processing large volumes of structured/unstructured dataExperience working with mortgage banking data sets and domain knowledge is highly preferredStrong understanding of data modeling (dimensional, normalized, star schema)Experience with cloud-based platforms (e.g., Azure Databricks, AWS EMR, GCP Dataproc)<ul><li>Familiarity with ETL tools, orchestration frameworks (e.g., Airflow, ADF, dbt)</li></ul>

Job Title: SQL Data EngineerLocation: Dallas, TX Education And/or Experience<ul><li>Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field</li><li>Advanced experience in SQL (e.g., CTEs, joins, text parsing) and experience building database objects (tables, stored procedures, views, Snowflake worksheets/notebooks)</li><li>2+ years of experience in data governance, data steward, data quality, or a related role</li><li>4+ years as a data analyst or data engineer, with strong analytical and problem-solving skills</li><li>Ability to quickly understand complex data relationships and derive actionable insights</li><li>Proven track record of meeting deadlines and communicating progress effectively</li><li>Strong collaboration and communication skills to work with technical and non-technical stakeholders</li><li>Excellent organizational and time management skills</li></ul>Preferred Qualifications<ul><li>Strong working experience with healthcare or financial data (e.g., claims, reimbursements)</li><li>Consulting experience, especially within Healthcare industry is a strong plus</li><li>Experience with Snowflake cloud data platforms</li><li>Hands-on experience documenting data governance policies in tools like Atlassian Confluence or Notion.</li><li>1+ years of experience building reports and dashboards in Power BI, Tableau, Grafana, or similar tools</li><li>Knowledge of ELT tools (e.g., DBT) and orchestration platforms (e.g., Airflow, Dagster)</li><li>Strong in SQL, SQL stored procedure scripting, amazing communication skills, able to work independently</li></ul>

Job Title: Data Engineer(python, Pyspark, data bricks)Location: Dallas, TX Data Engineer with strong proficiency in SQL, Python, and PySpark to support high-performance data pipelines and analytics initiatives. This role will focus on scalable data processing, transformation, and integration efforts that enable business insights, regulatory compliance, and operational efficiency.Data Engineer – SQL, Python and Pyspark Expert (Onsite – Dallas, TX)Key ResponsibilitiesDesign, develop, and optimize ETL/ELT pipelines using SQL, Python, and PySpark for large-scale data environmentsImplement scalable data processing workflows in distributed data platforms (e.g., Hadoop, Databricks, or Spark environments)Partner with business stakeholders to understand and model mortgage lifecycle data (origination, underwriting, servicing, foreclosure, etc.)Create and maintain data marts, views, and reusable data components to support downstream reporting and analyticsEnsure data quality, consistency, security, and lineage across all stages of data processingAssist in data migration and modernization efforts to cloud-based data warehouses (e.g., Snowflake, Azure Synapse, GCP BigQuery)Document data flows, logic, and transformation rulesTroubleshoot performance and quality issues in batch and real-time pipelinesSupport compliance-related reporting (e.g., HMDA, CFPB) Required Qualifications6+ years of experience in data engineering or data developmentAdvanced expertise in SQL (joins, CTEs, optimization, partitioning, etc.)Strong hands-on skills in Python for scripting, data wrangling, and automationProficient in PySpark for building distributed data pipelines and processing large volumes of structured/unstructured dataExperience working with mortgage banking data sets and domain knowledge is highly preferredStrong understanding of data modeling (dimensional, normalized, star schema)Experience with cloud-based platforms (e.g., Azure Databricks, AWS EMR, GCP Dataproc)Familiarity with ETL tools, orchestration frameworks (e.g., Airflow, ADF, dbt)

Question	Topic	Difficulty	Ask Chance
Boarding Times Bias	Statistics	Medium	Very High
Hundreds of Hypotheses	Statistics	Medium	Very High
Decreasing Comments	Product Metrics	Easy	Very High

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