Amgen is a leading biotechnology company dedicated to harnessing the best of biology and technology to develop innovative medicines for patients worldwide.
As a Data Engineer at Amgen, you will play a critical role in designing, building, and maintaining data pipelines that support the company’s mission to serve patients. You will be responsible for developing robust data solutions that facilitate the extraction, transformation, and loading (ETL) of large datasets. A key aspect of the role involves collaborating with cross-functional teams to identify data requirements, ensuring the quality and reliability of the data, and implementing data security measures. The ideal candidate will have strong technical expertise in big data technologies such as Apache Spark and AWS, along with proficiency in programming languages like Python and SQL. Experience with data modeling, data warehousing, and a solid understanding of cloud platforms are essential. Furthermore, a collaborative mindset and excellent problem-solving abilities are necessary traits to thrive in this dynamic environment.
This guide aims to equip you with the knowledge and insights to excel in your Amgen Data Engineer interview, helping you to showcase your skills effectively and align them with Amgen's mission and values.
In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Amgen. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data engineering concepts, particularly in relation to big data technologies, ETL processes, and cloud platforms.
Understanding the distinction between threads and processes is fundamental in data engineering, especially when dealing with concurrent data processing.
Discuss the definitions of threads and processes, emphasizing their differences in terms of memory allocation and execution.
“A process is an independent program that runs in its own memory space, while a thread is a smaller unit of a process that shares the same memory space. This means that threads can communicate more easily with each other than processes, but they also require careful management to avoid issues like race conditions.”
This question assesses your understanding of OOP, which is crucial for writing maintainable and scalable data engineering code.
Briefly define each principle and provide examples of how they can be applied in data engineering.
“The four principles of OOP are encapsulation, inheritance, polymorphism, and abstraction. For instance, encapsulation allows us to bundle data and methods that operate on that data within a single unit, which can help in managing complex data processing tasks.”
ETL (Extract, Transform, Load) processes are central to data engineering, and familiarity with various tools is essential.
Mention specific ETL tools you have used, describe your role in the ETL process, and highlight any challenges you faced.
“I have extensive experience with Apache Spark for ETL processes, where I designed and implemented data pipelines to extract data from various sources, transform it for analysis, and load it into a data warehouse. One challenge I faced was optimizing the performance of the ETL jobs, which I addressed by partitioning the data and using caching effectively.”
Data quality is critical in data engineering, and interviewers want to know your strategies for maintaining it.
Discuss methods you use to validate and clean data, as well as any tools that assist in this process.
“I implement data validation checks at various stages of the pipeline, such as schema validation and data type checks. Additionally, I use tools like Apache Airflow to monitor data quality and alert me to any discrepancies in real-time.”
Given that Amgen prefers candidates with cloud experience, this question is likely to come up.
Detail your experience with AWS services relevant to data engineering, such as S3, Redshift, and Lambda.
“I have worked extensively with AWS, particularly using S3 for data storage and Redshift for data warehousing. I have also utilized AWS Lambda for serverless data processing, which allowed me to run ETL jobs without managing servers, thus improving scalability and reducing costs.”
Understanding the differences between these two types of databases is essential for designing data solutions.
Define OLAP and OLTP, and explain their use cases in data engineering.
“OLAP (Online Analytical Processing) databases are optimized for read-heavy operations and complex queries, making them suitable for data analysis and reporting. In contrast, OLTP (Online Transaction Processing) databases are designed for transaction-oriented tasks, focusing on fast query processing and maintaining data integrity in multi-user environments.”
This question tests your knowledge of big data concepts, which are crucial for a data engineer.
Define the 3 Vs (Volume, Velocity, Variety) and provide examples of how they apply to data engineering.
“The 3 Vs of Big Data are Volume, which refers to the vast amounts of data generated; Velocity, which is the speed at which data is generated and processed; and Variety, which indicates the different types of data (structured, unstructured, semi-structured). For instance, in a healthcare setting, we deal with large volumes of patient data generated in real-time from various sources, including electronic health records and wearable devices.”
This question assesses your methodology in designing data models.
Discuss your process for understanding requirements, designing the model, and validating it.
“I start by gathering requirements from stakeholders to understand their data needs. Then, I create an initial conceptual model, followed by a logical model that defines the relationships between entities. Finally, I validate the model with stakeholders and iterate based on their feedback before implementing it in the database.”
Data warehousing is a key component of data engineering, and interviewers will want to know your experience.
Mention specific data warehousing solutions you have worked with and your role in their implementation.
“I have experience with Snowflake and Amazon Redshift for data warehousing. In my previous role, I was responsible for designing the data warehouse schema, implementing ETL processes to populate it, and optimizing query performance to ensure fast access to data for analytics.”
Schema changes can disrupt data pipelines, so interviewers want to know your strategies for managing them.
Discuss your approach to versioning, backward compatibility, and testing.
“When handling schema changes, I implement versioning for my data models to maintain backward compatibility. I also run tests to ensure that existing data pipelines continue to function correctly after the changes. Additionally, I communicate with stakeholders to inform them of any potential impacts on their data access.”
This question assesses your problem-solving skills and ability to work under pressure.
Provide a specific example, detailing the problem, your approach, and the outcome.
“In a previous project, we faced performance issues with our ETL pipeline due to a sudden increase in data volume. I analyzed the bottlenecks and identified that the transformation step was taking too long. I optimized the transformation logic and implemented parallel processing, which reduced the processing time by 50%.”
Time management and prioritization are crucial in a fast-paced environment.
Discuss your approach to assessing project urgency and importance.
“I prioritize tasks based on project deadlines and the impact of the data on business decisions. I use project management tools like Jira to track progress and communicate with my team to ensure alignment on priorities. Regular check-ins help me adjust priorities as needed.”
Collaboration is key in data engineering, and interviewers want to know your strategies for communication.
Discuss your methods for keeping stakeholders informed and engaged.
“I ensure effective communication by scheduling regular meetings with cross-functional teams to discuss project updates and gather feedback. I also use collaborative tools like Slack and Confluence to share documentation and updates, making it easy for everyone to stay informed.”
Mentorship is important for team development, and interviewers may want to know about your experience in this area.
Share a specific instance where you provided guidance and support.
“I mentored a junior data engineer who was struggling with SQL queries. I organized a series of one-on-one sessions where I explained best practices and provided hands-on exercises. Over time, I saw significant improvement in their skills, and they were able to contribute more effectively to our projects.”
This question assesses your commitment to continuous learning and professional development.
Discuss your methods for keeping your skills current, such as attending conferences, taking courses, or following industry publications.
“I stay updated by following industry blogs, participating in online forums, and attending webinars and conferences. I also take online courses to learn about new tools and technologies, ensuring that I can apply the latest best practices in my work.”
In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Amgen. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data engineering concepts, particularly in relation to big data technologies, ETL processes, and cloud platforms. Be prepared to demonstrate your knowledge through coding challenges and discussions about your past projects.
Understanding the distinction between threads and processes is fundamental in data engineering, especially when dealing with concurrent data processing.
Discuss the definitions of threads and processes, emphasizing their differences in terms of memory allocation and execution.
“A process is an independent program that runs in its own memory space, while a thread is a smaller unit of a process that shares the same memory space. This means that threads can communicate more easily with each other than processes, but they also require careful management to avoid issues like race conditions.”
This question assesses your understanding of OOP, which is crucial for writing maintainable and scalable data engineering code.
Briefly outline the four principles: encapsulation, inheritance, polymorphism, and abstraction, and provide a practical example of how you’ve applied them.
“The four principles of OOP are encapsulation, which restricts access to certain components; inheritance, which allows a class to inherit properties from another; polymorphism, which enables methods to do different things based on the object; and abstraction, which simplifies complex systems by modeling classes based on essential properties. For instance, I used encapsulation in a project to hide sensitive data processing logic from the user interface.”
ETL (Extract, Transform, Load) processes are central to data engineering, and familiarity with various tools is essential.
Discuss specific ETL tools you’ve used, your role in implementing ETL processes, and any challenges you faced.
“I have extensive experience with Apache Spark for ETL processes, where I designed data pipelines to extract data from various sources, transform it for analysis, and load it into a data warehouse. One challenge I faced was optimizing the performance of the ETL jobs, which I addressed by partitioning the data and using caching effectively.”
Data quality is critical in data engineering, and interviewers want to know your strategies for maintaining it.
Explain the methods you use to validate and clean data, as well as any tools that assist in this process.
“I ensure data quality by implementing validation checks at each stage of the ETL process. I use tools like Apache Airflow to monitor data flows and set up alerts for any anomalies. Additionally, I perform regular audits and use data profiling techniques to identify and rectify data quality issues proactively.”
Given that Amgen prefers candidates with cloud experience, be prepared to discuss your familiarity with cloud services.
Detail your experience with AWS services, including specific projects where you utilized these technologies.
“I have worked extensively with AWS, particularly with services like S3 for data storage, Redshift for data warehousing, and Lambda for serverless computing. In a recent project, I designed a data lake architecture on AWS that allowed for scalable data storage and processing, which significantly improved our data retrieval times.”
Understanding the differences between these two types of databases is crucial for data modeling.
Discuss the characteristics of OLAP and OLTP databases, including their use cases.
“OLAP (Online Analytical Processing) databases are optimized for read-heavy operations and complex queries, making them suitable for data analysis and reporting. In contrast, OLTP (Online Transaction Processing) databases are designed for transaction-oriented applications, focusing on fast query processing and maintaining data integrity. For instance, I used OLAP for a reporting system that required complex aggregations, while OLTP was used for our transactional systems.”
This question assesses your understanding of the data pipeline lifecycle and best practices.
Outline the steps you take in designing a data pipeline, from requirements gathering to deployment.
“My approach to designing a data pipeline starts with understanding the business requirements and data sources. I then design the architecture, focusing on scalability and performance. After implementing the pipeline using tools like Apache Spark, I ensure thorough testing and monitoring are in place before deployment. Finally, I document the process for future reference and maintenance.”
Schema changes can impact data pipelines significantly, so interviewers want to know your strategies for managing them.
Discuss your approach to versioning, backward compatibility, and communication with stakeholders.
“When handling schema changes, I follow a versioning strategy to ensure backward compatibility. I communicate with stakeholders to understand the impact of changes and implement them in a way that minimizes disruption. For instance, I might create a new version of the data model while keeping the old one active until all dependent systems are updated.”
Performance optimization is a key skill for data engineers, and this question allows you to showcase your problem-solving abilities.
Provide a specific example, detailing the query, the issues you identified, and the optimizations you implemented.
“I encountered a slow-running query that was affecting our reporting dashboard. I analyzed the execution plan and identified that missing indexes were causing full table scans. I added the necessary indexes and rewrote parts of the query to reduce complexity. After these changes, the query performance improved significantly, reducing execution time from several minutes to under 30 seconds.”
Data visualization is often a part of the data engineering role, and familiarity with tools is essential.
Discuss the visualization tools you’ve used and how you connect them to your data sources.
“I have experience with Tableau and Power BI for data visualization. I typically integrate these tools with our data pipelines by connecting them directly to our data warehouse, ensuring that the visualizations are always up-to-date. I also create views in the database specifically tailored for the visualizations to optimize performance and usability.”
Collaboration is key in data engineering, and this question assesses your interpersonal skills.
Share a specific example, focusing on the situation, your actions, and the outcome.
“I once worked with a team member who was resistant to adopting new technologies. I scheduled a one-on-one meeting to understand their concerns and shared the benefits of the new tools we were considering. By addressing their worries and involving them in the decision-making process, we were able to reach a consensus, and they became one of the strongest advocates for the new technology in our team.”
Time management is crucial in a fast-paced environment, and interviewers want to know your strategies.
Discuss your approach to prioritization, including any tools or methods you use.
“I prioritize my tasks by assessing the urgency and impact of each project. I use tools like Jira to track my tasks and deadlines, and I regularly communicate with my team to ensure alignment on priorities. For instance, if a project has a tight deadline that impacts a critical business function, I will allocate more time to it while ensuring that other projects are still progressing.”
This question assesses your adaptability and willingness to learn.
Share a specific example, focusing on the technology, your learning process, and the project outcome.
“I was tasked with implementing a new data processing framework using Apache Kafka, which I had not used before. I dedicated time to online courses and documentation, and I set up a small test environment to experiment with the technology. Within a few weeks, I was able to successfully integrate Kafka into our data pipeline, which improved our data processing speed significantly.”
Continuous learning is essential in tech roles, and interviewers want to know your strategies for staying informed.
Discuss the resources you use, such as blogs, courses, or communities.
“I stay updated by following industry blogs, participating in online forums, and attending webinars and conferences. I also take online courses on platforms like Coursera and Udacity to deepen my knowledge of emerging technologies. Recently, I completed a course on machine learning frameworks, which I found particularly relevant to my work in data engineering.”
This question assesses your motivation and alignment with the company’s mission.
Express your interest in Amgen’s mission and how your skills align with their goals.
“I am drawn to Amgen’s mission to serve patients and improve lives through innovative biotechnology. I believe my skills in data engineering can contribute to the development of data-driven solutions that enhance patient outcomes. I am excited about the opportunity to work in a collaborative environment that values innovation and continuous improvement.”
Here are some tips to help you excel in your interview.
The interview process at Amgen can be lengthy and may involve multiple rounds, including technical assessments and panel interviews. Be prepared for a mix of coding challenges, behavioral questions, and discussions about your past projects. Familiarize yourself with the structure of the interviews, as candidates have reported experiences ranging from phone screens to technical assessments on platforms like HackerRank. Knowing what to expect can help you manage your time and energy effectively.
As a Data Engineer, you will need to demonstrate proficiency in SQL, Python, and big data technologies such as Apache Spark and Databricks. Brush up on your coding skills, particularly with LeetCode-style problems, as many candidates have faced similar challenges during their interviews. Additionally, understanding ETL processes and data modeling concepts will be crucial. Practice live coding sessions to build confidence in articulating your thought process while solving problems.
Amgen values collaboration and teamwork, so be ready to discuss your experiences working in cross-functional teams. Prepare examples that showcase your problem-solving skills, adaptability, and ability to communicate effectively with diverse stakeholders. Reflect on times when you faced challenges in a team setting and how you contributed to overcoming them. This will help you align with Amgen's culture of collaboration and innovation.
During the interview, express your enthusiasm for data engineering and how it aligns with Amgen's mission to serve patients. Discuss any personal projects or experiences that highlight your commitment to leveraging data for impactful solutions. This will demonstrate your genuine interest in the role and the company, making you a more memorable candidate.
Amgen emphasizes a shared mission to serve patients and values collaboration among its employees. Research the company's recent initiatives and how they align with your values and career goals. Understanding the company's culture will not only help you answer questions more effectively but also allow you to assess if Amgen is the right fit for you.
Expect to dive deep into technical discussions, especially regarding your past projects and the technologies you've used. Be prepared to explain your decision-making process, the challenges you faced, and how you overcame them. This will demonstrate your expertise and ability to contribute to Amgen's data initiatives.
After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. This is not only courteous but also reinforces your interest in the position. Mention specific topics discussed during the interview to personalize your message and leave a lasting impression.
By following these tips, you can position yourself as a strong candidate for the Data Engineer role at Amgen. Good luck!
Here are some tips to help you excel in your interview.
The interview process at Amgen can be lengthy and involves multiple rounds, including technical assessments and panel interviews. Be prepared for a mix of coding challenges, behavioral questions, and discussions about your past projects. Familiarize yourself with the structure of the interviews, as candidates have reported experiences ranging from phone screens to technical assessments on platforms like HackerRank. Knowing what to expect can help you manage your time and energy effectively.
Given the emphasis on technical skills for the Data Engineer role, ensure you are well-versed in SQL, Python, and big data technologies such as Apache Spark and Databricks. Practice coding problems on platforms like LeetCode, focusing on medium to hard difficulty levels. Review key concepts in data architecture, ETL processes, and data modeling, as these are frequently discussed in interviews. Candidates have noted that questions often revolve around data manipulation, performance tuning, and system design, so be ready to demonstrate your problem-solving skills in these areas.
Amgen values teamwork and collaboration, so be prepared to discuss your experiences working in cross-functional teams. Highlight instances where you successfully collaborated with data scientists, business analysts, or other engineers to deliver data solutions. Emphasize your ability to communicate complex technical concepts to non-technical stakeholders, as this is crucial in a collaborative environment.
Amgen's mission to serve patients is central to its culture. During your interview, express your alignment with this mission and how your work as a Data Engineer can contribute to improving patient outcomes. Share examples of how your previous projects have had a positive impact, whether through efficiency improvements or innovative solutions. This will demonstrate your commitment to the company's goals and values.
Behavioral questions are a significant part of the interview process. Prepare to discuss your past experiences using the STAR (Situation, Task, Action, Result) method. Reflect on challenges you've faced, how you approached them, and what you learned from those experiences. Candidates have reported questions about teamwork, conflict resolution, and project management, so have relevant examples ready to share.
Amgen is at the forefront of biotechnology and data engineering. Show your enthusiasm for the field by discussing recent trends, technologies, or methodologies that excite you. This could include advancements in machine learning, data governance, or cloud technologies. Demonstrating your passion for continuous learning and innovation will resonate well with interviewers.
After your interview, send a thank-you email to express your appreciation for the opportunity to interview. Reiterate your interest in the role and briefly mention a key point from your discussion that reinforces your fit for the position. This not only shows professionalism but also keeps you top of mind as they make their decision.
By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Engineer role at Amgen. Good luck!
The interview process for a Data Engineer position at Amgen is structured and thorough, reflecting the company's commitment to finding the right talent for their innovative projects. The process typically includes several stages, each designed to assess both technical skills and cultural fit.
The first step in the interview process is a phone screen with a recruiter. This conversation usually lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Amgen. The recruiter will also discuss the role in detail and gauge your fit within the company culture. Expect questions about your availability and interest in the position.
Following the initial screen, candidates are often required to complete a technical assessment. This may take place on platforms like HackerRank or Karat and typically includes coding challenges that test your proficiency in SQL, Python, and data engineering concepts. The assessment may involve solving problems related to data manipulation, ETL processes, and big data technologies.
Successful candidates from the technical assessment will move on to a video interview with the hiring manager. This interview focuses on your technical expertise and experience in data engineering. You may be asked to discuss your previous projects, the tools and technologies you have used, and how you approach problem-solving in data-related challenges.
The next stage usually consists of one or more panel interviews. These interviews involve multiple team members, including data engineers, data architects, and possibly product managers. The panel will assess your technical skills through in-depth discussions about data architecture, ETL processes, and your experience with cloud platforms like AWS. Behavioral questions will also be included to evaluate your teamwork and communication skills.
In some cases, a final interview may be conducted with senior leadership or cross-functional team members. This interview is an opportunity for you to demonstrate your understanding of Amgen's mission and how your skills align with the company's goals. It may also involve discussions about your long-term career aspirations and how you can contribute to the team.
Throughout the interview process, candidates should be prepared to showcase their technical skills, problem-solving abilities, and collaborative mindset.
Next, let's explore the specific interview questions that candidates have encountered during their interviews at Amgen.
calculate_rmse
to calculate the root mean squared error of a regression model.The function should take in two lists, one that represents the predictions y_pred
and another with the target values y_true
.
Given a table of bank transactions with columns id
, transaction_value
, and created_at
, write a query to get the last transaction for each day. The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.
random_key
that returns a key at random with a probability proportional to the weights.Given a dictionary with weights, write a function random_key
that returns a key at random with a probability proportional to the weights.
Create a function to generate a sample from a standard normal distribution.
nearest_entries
to find the closest element to N
in a sorted list and return surrounding elements.Given a sorted list of integers ints
with no duplicates, write an efficient function nearest_entries
that takes in integers N
and k
and returns the element closest to N
along with the k
-next and k
-previous elements of the list.
Explain the concept of a p-value in simple terms to a non-technical person. Use analogies or everyday examples to make the explanation clear and relatable.
Given a sample size (n) with a margin of error of 3, calculate the additional number of samples required to reduce the margin of error to 0.3.
Describe the process of analyzing AB test results to determine if the observed differences in click-through rates are statistically significant. Include steps such as hypothesis testing and p-value calculation.
Netflix has two pricing plans: $15/month or $100/year. An executive wants to analyze the churn behavior of users subscribing to either plan. What kinds of metrics, graphs, and models would you build to provide an overarching view of subscription performance?
As a data scientist at DoorDash, how would you build a model to predict which merchants the company should target for acquisition when entering a new market?
Netflix executives are renewing a deal for exclusive streaming rights to a hit TV series. The show has been on Netflix for a year. How would you approach valuing the benefit of keeping this show on Netflix?
We want to launch a redesign of a landing page to improve the click-through rate using an AB test. How would you infer if the results of the click-through rate were statistically significant or not?
If you need to build Scrabble for Spanish users and don’t know Spanish, how would you determine the point values for each letter?
You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Amgen data engineer interview include:
Average Base Salary
Average Total Compensation
Amgen looks for candidates with strong technical skills, including experience with SQL, Python, AWS, data warehousing, and ETL processes. Knowledge of big data technologies such as Hadoop, Spark, and Kafka is also highly valued. Additionally, they seek candidates who can work effectively in a fast-paced, Agile environment and who possess strong problem-solving and communication skills.
The interview process at Amgen can be lengthy, often spanning several months from initial application to offer. Candidates have reported multiple rounds of interviews spread out over a few months, with occasional delays in feedback between stages.
Amgen fosters a collaborative, innovative, and inclusive culture focused on serving patients. Employees are encouraged to work together to research, manufacture, and deliver better products. The company values creativity, accountability, and continuous improvement, and provides various benefits and opportunities for career development.
Given the meticulous recruiting process, it’s clear that Amgen seeks individuals who are not only technically proficient but also dedicated and resilient. The path could involve several technical interviews focusing on Python, SQL, AWS, and system design, followed by extensive panel discussions. For many, this journey can be lengthy, but the experience gained and the impact you can have on a global scale make it worthwhile.
If you’re interviewing for a Data Engineer position at Amgen, preparation is key. Be ready to showcase your technical abilities, problem-solving skills, and experience with large-scale data systems. At Interview Query, we have assembled comprehensive guides for Amgen interview processes. To gain invaluable insights, check out our detailed Amgen Interview Guide, which covers a variety of interview questions and best practices tailored specifically for roles at Amgen.
Good luck with your interview!