You're preparing for a data engineering interview. How do you make sure you're ready for anything?

While preparing for a data engineering interview, ensure that you understand two essential concepts: ETL and Data Warehousing. Regardless of the ETL tool or Cloud ETL services, understanding the fundamental ETL principles, such as Source details (Type, Volume, Frequency), transformations required, and various data load strategies (SCD's), is critical for building an end-to-end ETL pipeline for every individual use case. Data modeling skills improve with experience, but many people still fail to answer basic questions, therefore it's always a good idea to brush up on basic concepts before attending an interview. Given the current demand for Data Engineering, it is important to practice languages such as SQL, Python, PySpark.

Share instances where you spearheaded data engineering projects or drove positive changes. Highlight your knack for dissecting complex problems and identifying root causes. Master SQL scripting for efficient data manipulation and extraction. Dive into ETL scenarios, crafting transformation and integration scripts. Tackle data modeling challenges, optimizing schema designs. Engage in online platforms, coding challenges, and review sample data warehousing problems.

Start your preparation with the things that you have mentioned on your CV. Be thorough with the tools and technologies mentioned on there. Prepare to discuss the projects you have worked on. Take care not reveal any business sensitive information. Be thorough on the principles, not just technologies. Also read about other technologies mentioned in the job spec.

On a high level a data engineer's job is move data from Source to Target. So, be sure to cover :- i) Source -- Source data , source format, Scheduling of the pipeline, Volume of data, latency of data ii) Processing --- Transformation, Joining with reference data, Joinable column, Processing engine, DQ check, redistribution if pipeline fails iii) Target -- Target source, Target schema , Target access control, Transactional warehouse design Then prepare for tools to help it - Apache Spark, Delta, AWS Glue, AWS EMR GCP dataproc , large scale databases like Redshift, BigQuery, Snowflake

- Solid grasp of data modeling, database design, SQL and data warehousing principles. Understand basics of distributed systems, especially as they relate to big data technologies like Hadoop, Spark, and Kafka. - Be comfortable with Python or Java, as they're commonly used in data engineering. Practice writing scripts that manipulate data and familiarize with libraries and frameworks. -Understand ETL processes in-depth. Be able to discuss and design ETL pipelines that clean, aggregate, and move data from various sources to databases or warehouses. - Familiarity with AWS, GCP, Azure cloud services, especially related to data storage and orchestration like AWS S3, EC2, RDS, Google Cloud Storage, BigQuery, Azure Blob Storage, & SQL Database.

Nailing the coding aspect is non-negotiable for data engineering roles. Familiarity with your preferred programming languages and tools is key, as is the ability to tackle typical data engineering challenges—think data cleaning, manipulation, and optimization—under interview pressure. Platforms like LeetCode, HackerRank, and DataCamp are goldmines for practice questions and scenarios specific to data engineering. Diving into these resources helps not just in sharpening your coding skills but also in getting comfortable with the kind of problems you'll face in interviews. Remember, consistent practice is what bridges the gap between knowing the theory and being able to apply it effectively when it matters most.

For me I always give a small sample whiteboard code challenge. which is not challenging. if it's a question you heard a million times i.e. write a "fizz buzz" solver in TSQL don't roll your eyes. we know you don't care and neither do we about the answer. I really only care about your thought process and how you got to your answer. in programming there are usually multiple ways to skin the proverbial cat. focus on optimization and talk through the problem out loud. if you have questions, ask. lastly if something in the challenge isn't possible SAY SO. we know some stuff can't be done we just want to know how you think about and approach a problem.

Focus on the programming languages commonly used in data engineering, such as Python, Java, Scala, or even SQL. Understand how to optimize code for performance, especially in distributed computing environments.

La primera impresión es muy importante, por lo que una buena práctica profesional es tener como demostrar las habildades, y para ello githib y/o disponer de una pagina a manera de portafolio.

Focus on sql, python and scala, used leed code medium questions, try to figure out patterns mosly asked. Please check the discussion segment you get to how others are thinking, it will help to see the problems from various point of view

- With your weak skill, please provide the solution to deal with it - not just mention. - With your strong skill, talk about them with how you use it in your projects/ company.

Share examples of times when you took the initiative to lead a data engineering initiative or drive a positive change. Highlight your ability to break down complex problems, identify root causes, and develop effective solutions.

Lo mas "corchador" es cuando nos preguntan por nuestras debilidades, si bien es cierto que hay que tener a la mano la respuesta por aquellos casos dificiles que logramos sacar adelante, nos pueden preguntar por las fuistraciones profesionales que hemos vivido y como las hemos manejado.

It’s easy to write the code, I feel behavioural question are amazing opportunity to show case your Communication skills and problem solving skills. Share how did you came up with the required answer based your past experience and research. What’s unique in your solution and why do you feel it would be best. Think holistically from optimistic, simple and well audited solutions.

Prepare by recalling past experiences. Identify key projects, challenges faced, and results achieved. Practice expressing your role, actions taken, and the impact on project outcomes. Concentrate on teamwork, communication, and problem-solving skills. Align answers with the specific skills the interviewer seeks. There are high chances of situational queries about handling deadlines, conflicts, or unexpected issues in any interview.

You’re lucky to have Data in your job title, so don’t attend interviews without knowing and researching company use case and data domain. These days most employers publish business domain and specific applicable vision within the job description. So knowing every bit of data will improve your confidence as well as help you identify values you will bring it on the table.

Dont attend the interview without knowing the company and its business as well as the interviewer background and the role youbwill play and expected deliverables. That will help you to prepare scenarios fitting the company business and understand the mindset of your interviewer and the point of focus by the interviewer.

Read the job description thoroughly. Pick key areas where you can talk and show how you can add value to the organisation. Frame all your answers to fit the description.

Data consumed and extracted is different for business to business. It’s very important to understand the Business you’re applying for how big is the data infrastructure. Do a rough estimation of data the business would be managing in a day, month and in a year. What’s the mission and vision of company in terms if data, it’s easy to research and a brownie discussion to share with interviewer.

Try to get as much information about the company by looking up on their website and social media. Look at the job spec and try to understand where you will fit in the business process. Use LinkedIn to look for the profile of the company's Data Engineers past and present. This should help you prepare. Do not hesitate to contact someone in your network who you think might throw some light on the role. Use this information to prepare your answers, and also ask questions to the interview panel on any aspect you would like to know about.

Currently most interviews are scheduled online and it is very important to ensure a seamless internet connection and a good camera along with a silent and well lit room. It is also very important to make sure that the room you are in doesn't make echos. Make sure it is away from the sound of pets, kids and kitchen equipments. When it comes to in person interviews always be dressed for the occasion. A formal outfit can never go wrong. Also make sure you are well groomed and give out a sharp look. Trust me looks are important in an interview.

Know your space well, if you have been assigned an environment under it check all the table and schema. Make a list of question which you would like to understand before you start the environment. Instead of asking question on the fly, asking upfront puts you in a better positions shows your able to ask questions and challenge. Once you have all the answer give your best to answer if you finish fantastic you have not don’t worry share you have understood what have you done what you wanted to do and why.

Por supuesto que es fundamental una planeación, estamos jablando de lo que puede llegar a ser un imporrante cambio en nuestra carrera y no debemos dejarlo al azar.

Test your environment before the interview starts. That will give you a scene of familiarity and confidence. At the time of solving try to think out load so that he van understand how are you thinking, most of the time they help to with some hints

In all the data interviews data engineer interviews needs a lot of time to prepare. Start early in your journey, start preparing earlier. Plan the interview phase what you want to learn before going for your 1st interview. Remember to note down your learning from each interview. The best points, points which could have been improved and don’t be harsh on yourself because data engineering is difficult interview to clear.

This is my personal experience as i already have faced rejection in interviews, especially multiple times with the same company like Teradata, which was disheartening. However, remember that growth often comes from setbacks, and sometimes it's a matter of timing and circumstances beyond your control. Trust the process, learn from each experience, and stay persistent in your pursuit.

It is a complex question... There are 3 fundamental topics in the DWH modeling - Kimball, Inmon & Data Vault. Data engineering for BigData, BI, Cloud, IoT, AI, Data lake design patterns and principles etc. itself are big topics. Languages and platforms like Python, Kafka, Jupiter or Data Science and Applied Mathematics etc. itself are another universes. There are a lot of Apache projects in this domain. Make sure that you try some ETL tools and impress the potential employer by showing different use cases, demos or practice examples. Do your homework and research prior going on interview. Certificates also count. Data is the new currency and if you want stable career in this area be optimistic, consistent and never stop learning. Good luck!

The best way to be ready for anything begins with knowing that nobody, ever, is ready for anything. Adding more words to this only makes it less elegant.

Data engineering is not just about technical skills, have some scenarios from your experience ready to talk about. For example, challenges that you had and overcame, how you communicated effectively, biggest achievement and how you dealt with negative feedback.

Find out who is interviewing do some research on them. They likely have a slant to a technology or two. Look at your network and see if there is anyone there already.