Information Resource Group (IRG) is a leading provider of comprehensive IT services and solutions, specializing in a wide range of technology-driven offerings. Renowned for their expertise in data management and system integration, IRG consistently delivers innovative and efficient solutions to meet their clients’ needs.

The Data Engineer position at IRG is a highly dynamic role focused on creating ETL processes to migrate data into new data warehouse formats. This includes building Synapse pipelines to transition data into an Azure data lake. The role involves advanced T-SQL development, data mapping between transactional and dimensional systems, and working closely with administrators and vendors for efficient pipeline creation. Essential skills include a deep understanding of data warehouse design, proficiency in T-SQL, and experience with ALM tools and continuous integration.

For those aspiring to join IRG, our guide on Interview Query will walk you through the interview process, essential skills, and provide valuable tips for this position. Let's get started!

Submitting Your Application

The first step to securing a Data Engineer position at Information Resource Group is to submit a compelling application that highlights your technical skills and keen interest in joining the company. Whether you were contacted by a recruiter or applied proactively, it is crucial to review the job description carefully and tailor your CV to meet the stated prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Moreover, highlight relevant skills and mention your work experiences pertaining to SQL, ETL processes, Synapse pipelines, and Azure Data Engineer responsibilities.

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, a recruiter from Information Resource Group will reach out to verify key details such as your experiences and skill level. Expect behavioral questions to be part of the screening process.

Typically, the hiring manager for the Data Engineer role at Information Resource Group may also join the call to answer any questions you have about the role and the company. They may delve into surface-level technical and behavioral discussions to gauge your fit for the position.

The entire recruiter call should take around 30 minutes.

Technical Virtual Interview

If you successfully pass the recruiter screening, you will be invited to a technical screening round. The technical screening for the Data Engineer position usually occurs through virtual means, including video conferencing and screen sharing. This 1-hour long interview session will likely focus on SQL queries, ETL pipelines, and Synapse pipelines.

You may also receive take-home assignments related to data warehouse design, T-SQL development, and migrating data to Azure data lakes. Your proficiency in hypothesis testing, probability distributions, and database modeling could be assessed.

Onsite Interview Rounds

Upon succeeding in the previous rounds, you will be invited to attend an onsite interview loop. During your visit to the Information Resource Group office, multiple interview rounds will be conducted to evaluate your technical skills, including your competency in programming and data modeling.

If you were given take-home exercises, prepare for a presentation round where you’ll discuss your approach and results. Continuing with the onsite interview, expect in-depth discussions on your experience with data warehouse design, T-SQL, and relevant Azure services.

Quick Tips for Information Resource Group Data Engineer Interviews

• Deep Dive into Azure Services: Ensure you are well-versed with Azure Data Services, specifically Synapse pipelines and data lakes. Understanding how to effectively utilize these services will give you an edge.

• Master T-SQL and ETL Processes: Given the job description, extensive experience with T-SQL development and ETL processes is crucial. Practice creating and optimizing stored procedures and familiarizing yourself with different ETL tools.

• Know Data Warehouse Hierarchies: A thorough understanding of data warehouse design hierarchies, such as star and snowflake schemas, is essential. Brush up on these concepts and be ready to discuss your experience with them.

Good luck with your interview preparation!

Typically, interviews at Information Resource Group vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to find the maximum number in a list of integers. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

• Create a function convert_to_bst to convert a sorted list into a balanced binary tree. Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should be balanced, meaning the height difference between the left and right subtree of all the nodes should be at most one.

• Write a function to simulate drawing balls from a jar. Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

• Develop a function can_shift to determine if one string can be shifted to become another. Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

• What are the drawbacks of the given data organization, and how would you reformat it for better analysis? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in "messy" datasets.

• How would you locate a mouse in a 4x4 grid using the fewest scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. Describe a strategy to find the mouse using the fewest number of scans.

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in NYC and Charlotte and needs a process for selecting dashers. Describe how you would decide which dashers to select and whether the criteria would be the same for both cities.

• What factors could bias Jetco's study on boarding times, and what would you investigate? Jetco, a new airline, has the fastest average boarding times according to a study. Identify potential biases in this result and describe what factors you would investigate.

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? A B2B SAAS company wants to test different subscription pricing levels. Describe how you would design a two-week A/B test to evaluate a pricing increase and determine if it is a good business decision.

• How much should we budget for the coupon initiative in total? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider. The app services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of both riders getting the coupon? A driver using the app picks up two passengers. Determine the probability that both riders will receive the coupon.

• What is the probability that only one of them will get the coupon? A driver using the app picks up two passengers. Determine the probability that only one of the riders will receive the coupon.

• What is a confidence interval for a statistic? Explain what a confidence interval is, why it is useful, and how to calculate it.

• What is the probability that item X would be found on Amazon's website? Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that item X is available at warehouse A (0.6) and warehouse B (0.8), calculate the probability that item X would be found on Amazon's website.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair.

• What are time series models and why do we need them? Describe what time series models are and explain why they are necessary when simpler regression models exist.

• How would you explain linear regression to a child, a college student, and a mathematician? Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to each audience's understanding level.

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? As a data scientist at a bank, you need to build a decision tree model to predict if a borrower will repay a personal loan. Evaluate whether a decision tree is the correct model and how you would assess its performance before and after deployment.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. Justify the complexity of the model and explain its predictions to non-technical stakeholders.

• How does random forest generate the forest, and why use it over logistic regression? Explain how random forest generates its forest and discuss why it might be preferred over other algorithms like logistic regression.

• What are the key differences between classification models and regression models? Describe the main differences between classification models and regression models.

What is the average salary for a Data Engineer at Information Resource Group?

Q: What does the role of an Azure Data Engineer at Information Resource Group entail? The Azure Data Engineer role at Information Resource Group involves creating ETL processes to migrate data into a new data warehouse format, building Synapse pipelines to move data from on-premises to an Azure data lake, and developing stored procedures. The position requires a thorough understanding of data warehouse design hierarchies like star and snowflake schemas and collaborating with vendors and team members efficiently.

Q: What are the required skills for the Azure Data Engineer position? Candidates should have extensive experience in developing stored procedures and ETL processes using T-SQL, a strong understanding of data warehouse design hierarchies, and knowledge of ALM tools for work item management and version control. Additionally, familiarity with continuous integration, experience in creating and scheduling elastic jobs, and proficiency in designing and modeling database structures based on business use cases are essential.

Q: What is the work environment like for this position? The Azure Data Engineer role is remote, offering flexibility and the ability to work independently while also requiring effective collaboration as part of a team. The role demands the ability to work under severe time constraints and requires strong analytical and problem-solving skills.

Q: What is the duration of the Azure Data Engineer position? The position is contracted for 1.5 years, providing a significant period to contribute to and gain extensive experience from the project.

Q: How can I prepare for an interview at Information Resource Group for the Azure Data Engineer role? To prepare for an interview at Information Resource Group, you should review the job description and required skills thoroughly. Focus on brushing up your knowledge of T-SQL, data warehouse design principles, and Synapse pipelines. Additionally, practice common interview questions on Interview Query to enhance your technical and problem-solving abilities.

Exploring a career as an Azure Data Engineer at Information Resource Group promises a dynamic and challenging role, especially if you excel in T-SQL development and ETL processes. From crafting ETL pipelines to transitioning data from on-premises systems into Azure's data lake, this position offers a comprehensive set of responsibilities for those passionate about data management.

If you want more insights about the company, check out our main Information Resource Group Interview Guide, where we have covered many interview questions that could be asked. At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!