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Egen Interview Questions

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 that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Experiment Validity

A/B Testing & Experimentation

Egen Salaries by Position

Job Overview: We are looking for a skilled and motivated Lead Data Engineer with strong experience in Python programming and Google Cloud Platform (GCP) to join our data engineering team. The ideal candidate will be responsible for requirements gathering, designing, architecting the solution, developing, and maintaining robust and scalable ETL (Extract, Transform, Load) & ELT data pipelines. The role involves working with customers directly, gathering requirements, discovery phase, designing, architecting the solution, using various GCP services, implementing data transformations, data ingestion, data quality, and consistency across systems, and post post-delivery support. Experience Level: 10 to 12 years of relevant IT experienceKey Responsibilities:<ul><li>Design, develop, test, and maintain scalable ETL data pipelines using Python.</li><li>Architect the enterprise solutions with various technologies like Kafka, multi-cloud services, auto-scaling using GKE, Load balancers, APIGEE proxy API management, DBT, using LLMs as needed in the solution, redaction of sensitive information, DLP (Data Loss Prevention) etc.</li><li>Work extensively on Google Cloud Platform (GCP) services such as:</li><li>Dataflow for real-time and batch data processing</li><li>Cloud Functions for lightweight serverless compute</li><li>BigQuery for data warehousing and analytics</li><li>Cloud Composer for orchestration of data workflows (on Apache Airflow)</li><li>Google Cloud Storage (GCS) for managing data at scale</li><li>IAM for access control and security</li><li>Cloud Run for containerized applications</li><li>Should have experience in the following areas : </li><li>API framework: Python FastAPI</li><li>Processing engine: Apache Spark</li><li>Messaging and streaming data processing: Kafka</li><li>Storage: MongoDB, Redis/Bigtable</li><li>Orchestration: Airflow</li><li>Experience in deployments in GKE, Cloud Run.</li><li>Perform data ingestion from various sources and apply transformation and cleansing logic to ensure high-quality data delivery.</li><li>Implement and enforce data quality checks, validation rules, and monitoring.</li><li>Collaborate with data scientists, analysts, and other engineering teams to understand data needs and deliver efficient data solutions.</li><li>Manage version control using GitHub and participate in CI/CD pipeline deployments for data projects.</li><li>Write complex SQL queries for data extraction and validation from relational databases such as SQL Server, Oracle, or PostgreSQL.</li><li>Document pipeline designs, data flow diagrams, and operational support procedures.</li></ul>Required Skills:<ul><li>10 to 12 years of hands-on experience in Python for backend or data engineering projects.</li><li>Strong understanding and working experience with GCP cloud services (especially Dataflow, BigQuery, Cloud Functions, Cloud Composer, etc.).</li><li>Solid understanding of data pipeline architecture, data integration, and transformation techniques.</li><li>Experience in working with version control systems like GitHub and knowledge of CI/CD practices.</li><li>Experience in Apache Spark, Kafka, Redis, Fast APIs, Airflow, GCP Composer DAGs.</li><li>Strong experience in SQL with at least one enterprise database (SQL Server, Oracle, PostgreSQL, etc.).</li><li>Experience in data migrations from on-premise data sources to Cloud platforms.</li></ul>Good to Have (Optional Skills):<ul><li>Experience working with the Snowflake cloud data platform.</li><li>Hands-on knowledge of Databricks for big data processing and analytics.</li><li>Familiarity with Azure Data Factory (ADF) and other Azure data engineering tools.</li></ul>Additional Details:<ul><li>Excellent problem-solving and analytical skills.</li><li>Strong communication skills and ability to collaborate in a team environment.</li></ul>Education:<ul><li> Bachelor's degree in Computer Science, a related field, or equivalent experience.</li></ul>

<ul><li>Design, develop, and maintain ETL/ELT data pipelines using Python and AWS native services (Glue, Lambda, EMR, Step Functions, etc.)</li><li>Build and manage data lakes and data warehouses using Amazon S3, Redshift, Athena, and Lake Formation</li><li>Implement data ingestion from diverse sources (RDBMS, APIs, streaming data, on-premise systems)</li><li>Optimize data workflows for performance, cost, and reliability using AWS tools like Glue Jobs, Athena, and Redshift Spectrum</li><li>Develop reusable, modular Python-based frameworks for data ingestion, transformation, and validation</li><li>Work with stakeholders to understand data requirements, model data structures, and ensure data consistency and governance</li><li>Deploy and manage data infrastructure using Infrastructure as Code (IaC) tools such as Terraform or AWS CloudFormation</li><li>Implement data quality, monitoring, and alerting using CloudWatch, Glue Data Catalog, or third-party tools</li><li>Support data security and compliance (IAM roles, encryption, data masking, GDPR policies, etc.)</li><li>Collaborate with DevOps and ML teams to integrate data pipelines into analytics and AI workflows</li></ul>Required Skills & Qualifications<ul><li>Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field.</li><li>Minimum 5 to 8 years of experience as a Data Engineer or similar role.</li><li>Strong programming experience in Python (pandas, boto3, PySpark, SQLAlchemy, etc.)</li><li>Deep hands-on experience with AWS services, including:</li><li>AWS Glue, Lambda, EMR, Redshift, Athena, S3, Step Functions</li><li>IAM, CloudWatch, Kinesis (for streaming), and ECS/EKS (for containerized workloads)</li><li>Experience with SQL and NoSQL databases (e.g., PostgreSQL, DynamoDB, MongoDB)</li><li>Strong knowledge of data modeling, schema design, and ETL orchestration.</li><li>Familiarity with version control (Git) and CI/CD pipelines for data projects.</li><li>Understanding of data governance, lineage, and cataloging principles.</li><li>Excellent problem-solving, debugging, and performance-tuning skills.</li></ul>

Job Overview:We are looking for a skilled and motivated Lead Data Engineer with strong experience in Python programming and Google Cloud Platform (GCP) to join our data engineering team. The ideal candidate will be responsible for requirements gathering, designing, architecting the solution, developing, and maintaining robust and scalable ETL (Extract, Transform, Load) & ELT data pipelines. The role involves working with customers directly, gathering requirements, discovery phase, designing, architecting the solution, using various GCP services, implementing data transformations, data ingestion, data quality, and consistency across systems, and post post-delivery support. Experience Level: 10 to 12 years of relevant IT experienceKey Responsibilities:<ul><li>Design, develop, test, and maintain scalable ETL data pipelines using Python.</li><li>Architect the enterprise solutions with various technologies like Kafka, multi-cloud services, auto-scaling using GKE, Load balancers, APIGEE proxy API management, DBT, using LLMs as needed in the solution, redaction of sensitive information, DLP (Data Loss Prevention) etc.</li><li>Work extensively on Google Cloud Platform (GCP) services such as:</li><li>Dataflow for real-time and batch data processing</li><li>Cloud Functions for lightweight serverless compute</li><li>BigQuery for data warehousing and analytics</li><li>Cloud Composer for orchestration of data workflows (on Apache Airflow)</li><li>Google Cloud Storage (GCS) for managing data at scale</li><li>IAM for access control and security</li><li>Cloud Run for containerized applications</li><li>Should have experience in the following areas :</li><li>API framework: Python FastAPI</li><li>Processing engine: Apache Spark</li><li>Messaging and streaming data processing: Kafka</li><li>Storage: MongoDB, Redis/Bigtable</li><li>Orchestration: Airflow</li><li>Experience in deployments in GKE, Cloud Run.</li><li>Perform data ingestion from various sources and apply transformation and cleansing logic to ensure high-quality data delivery.</li><li>Implement and enforce data quality checks, validation rules, and monitoring.</li><li>Collaborate with data scientists, analysts, and other engineering teams to understand data needs and deliver efficient data solutions.</li><li>Manage version control using GitHub and participate in CI/CD pipeline deployments for data projects.</li><li>Write complex SQL queries for data extraction and validation from relational databases such as SQL Server, Oracle, or PostgreSQL.</li><li>Document pipeline designs, data flow diagrams, and operational support procedures.</li></ul>Required Skills:<ul><li>10 to 12 years of hands-on experience in Python for backend or data engineering projects.</li><li>Strong understanding and working experience with GCP cloud services (especially Dataflow, BigQuery, Cloud Functions, Cloud Composer, etc.).</li><li>Solid understanding of data pipeline architecture, data integration, and transformation techniques.</li><li>Experience in working with version control systems like GitHub and knowledge of CI/CD practices.</li><li>Experience in Apache Spark, Kafka, Redis, Fast APIs, Airflow, GCP Composer DAGs.</li><li>Strong experience in SQL with at least one enterprise database (SQL Server, Oracle, PostgreSQL, etc.).</li><li>Experience in data migrations from on-premise data sources to Cloud platforms.</li></ul>Good to Have (Optional Skills):<ul><li>Experience working with the Snowflake cloud data platform.</li><li>Hands-on knowledge of Databricks for big data processing and analytics.</li><li>Familiarity with Azure Data Factory (ADF) and other Azure data engineering tools.</li></ul>Additional Details:<ul><li>Excellent problem-solving and analytical skills.</li><li>Strong communication skills and ability to collaborate in a team environment.</li></ul>Education:<ul><li>Bachelor's degree in Computer Science, a related field, or equivalent experience.</li></ul>

Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. You will join a team of insatiably curious data engineers, software architects, and product experts who never settle for "good enough". Our Java Platform team's tech stack is based on Java8 (Spring Boot) and RESTful web services. We typically build and deploy applications as cloud-native Kubernetes microservices and integrate with scalable technologies such as Kafka in Docker container environments. Our developers work in an agile process to efficiently deliver high value data driven applications and product packages. Required Experience:<ul><li>Have 5 years of experience working and strong understanding of object-oriented programming and cloud technologies</li><li>End to end experience delivering production ready code with Java8, Spring Boot, Spring Data, and API libraries</li><li>Strong experience with unit and integration testing of the Spring Boot APIs.</li><li>Strong understanding and production experience of RESTful API's and microservice architecture.</li><li>Strong understanding of SQL databases and NoSQL databases and experience with writing abstraction layers to communicate with the databases.</li><li>Minimum of Bachelor’s Degree or its equivalent in Computer Science, Computer Information Systems, Information Technology and Management, Electrical Engineering or a related field.</li><li> </li><li> Nice to have's (but not required):</li><li> </li><li>Strong understanding and production experience working with Docker container environments</li><li>Strong understanding and production experience working with Kafka </li><li>Cloud Environments: AWS, GCP or Azure</li><li>Exposure to Kotlin or other JVM programming languages</li></ul>

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

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