Data engineering jobs are often highly competitive as they are one of the most sought-after careers globally. The range of technical skill sets needed for the job is high, often leaving candidates confused while preparing for a job interview. While some aspirants for this role focus on learning newer tools and platforms, some develop a sound business foundation. So how does one prepare for these interviews for data engineering jobs? This article focuses on this topic and offers essential tips to help you better prepare for the interview:
Before the interview
#1: Take time to Understand the Job Profile
To begin with, while applying for the job, understand the job description to figure out what the job entails. Then, think through which courses, projects, and scenarios are relevant to the responsibilities mentioned in the job description. It is natural that you may forget something from your past, especially things that happened a while back. But if you have mentioned it in your resume, be prepared to answer questions about it.
#2: Learn About the Company You Have Applied For
Understand more about the company you are interviewing for – their website is a great place to start. Put yourself in the interviewer’s chair and think about what questions they might ask you. Job search websites like Glassdoor are valuable resources for finding interview questions for specific companies. In addition, it would help to talk to friends and colleagues who are data experts to understand what their job profile looks like and what are some of the common challenges they face at work.
#3: Revise Your Core Skills
As a data engineer, you may be required to know one or more programming languages like Java, Python, SQL, Unix/Linux, and R. Understand the job description and revise the expected technical skills needed for the profile. For instance, if the job focuses on a backend-centric system, you may want to prepare on Scala or Python. Also, review and highlight the technical concepts like distributed systems & computing engines, MPP (massively parallel processing) databases, and event-driven systems that may be required for the job.
Review data pipeline systems and new tools and features across big data platforms, especially in the Hadoop ecosystem. Apache Spark is popular amongst the data engineering community and the next big thing to learn for any data engineer.
#4: Know about the nice-to-have skills:
As a data engineer, it is an added advantage to know the basics of one or more of the following :
- Modern data architectures
- Real-time data processing using tools like Apache Kafka
- Workflow tools such as Apache Airflow
- No-SQL databases like Cassandra, HBase, MongoDB
- Cloud platforms like Microsoft Azure or AWS, or GCP
- Modern DBaaS (Database-as-a-service) platforms like Databricks and Snowflake
- Code repository and version control using tools like Git, Bitbucket
- Data pipeline automation using Machine learning and Artificial Intelligence techniques
While this is an elaborate list, focus on the ones mentioned in your job description.
#5: Prepare for Scenario-based Questions
To make the discussion effective, identify an end-to-end data flow scenario from your experience and prepare to speak about it. Make sure to state the goal clearly and how you handled data lineage, duplication, loading data, scaling, testing, and end-user access patterns. Talk about how the pipeline made data accessible to multiple data-consuming applications through well-maintained and reliable endpoints. You should be able to talk fluently about different phases of a data pipeline, such as data ingestion, data processing, and data visualization. You should also explain how different frameworks work together in one data pipeline. At the same time, highlight points such as data quality, security, and how you improved the availability, scalability, and security of the data pipelines for on-prem or cloud-based applications. This will give a holistic picture to the panelists.
#6: Communication is Key
Learn how to explain your past projects in technical and business terms. Aside from being able to code and assemble data, you must also be able to describe your approach and methodology to the interviewers. Also, practice speaking about your choices and why you chose a particular approach or tool over another.
Interviewers will always look for how well you represent any business scenario and how confidently you can speak about the projects you have worked on. A good way to practice is to do a mock-up session with a friend unfamiliar with big data.
During the interview
#7: Provide Contextual Answers – This is the best way to showcase your analytical and problem-solving skills. Having the ability to quickly produce a viable solution to any problem shows the recruiter that you can handle tough situations. Backing this with experience will help you stand out from the competition. For example, an interviewer might ask:
When did you last face a problem managing unstructured data, and how did you resolve it?
They want to know your way of dealing with problems and how you use your strengths to solve data engineering issues. First, give them a brief background about the problem and how it came to be, then briefly talk about what processes and technologies you used to disentangle it—and why you chose them.
#8: Demonstrate your Problem-Solving and Technical Skills
If you are asked a scenario-based question, first understand the question well before you answer it. Scenario-based questions can be tricky, and the panelists may want to evaluate your analytical abilities by posing questions that do not provide complete clarity. In such a scenario, asking the panelists additional questions if needed is the best strategy to be clear on the question before you choose to answer. Sometimes there is no right or wrong answer to such questions. The interviewer is most likely testing your approach rather than the solution itself.
While answering a scenario-based question, try to demonstrate your technical skills wherever applicable.
#9: Be Ready to Code
Some interviewers may ask you to quickly write a function to modify the input data and generate the desired output data. You will be expected to employ the most effective data structures and algorithms and handle all potential data concerns nimbly and efficiently. Even if you cannot write the code by maintaining the proper syntax, pseudo-code also works in most cases. Interviewers would look at the logic you have used to build the code.
In the real world, data engineers do not just utilize the Company’s built-in libraries but often use open-source libraries too. You may be asked to design solutions utilizing well-known open-source libraries like Pandas and Apache Spark in your coding interview. You will probably be given the option of looking up resources as needed. If the position demands expertise in specific technologies, be prepared to use them during your coding interview.
#10 Finally Relax!
It is natural to get caught up in the questions and feel intimidated by the person across the table. But do not lose sight of the fact that your interviewer wants you to do well. They want to hire someone exceptional for the position—and they hope you are that someone. Go into the interview with the right mindset and prepare a few questions to ask the interviewer when you get a chance.
Interested in working on complex data engineering projects? Apply to Infocepts today
Data and analytics has become the bedrock of business strategies helping companies understand their customers, build better products, save costs, provide higher-quality services and transform their businesses. With the explosion in user-generated data and businesses wanting to deliver the right products at the right time, the data analytics industry has exploded as a career path. Job roles such as data engineers, analytics cloud professionals, data scientists, AI and machine learning engineers are in high demand today.
According to Nasscom, the demand for digital talent jobs in India is around eight times bigger than the size of its fresh talent pool and skills such as big data, analytics, cloud computing, mobility, machine learning and cyber security are in great demand.
To keep up, businesses will seek full-stack engineers who will be able to create the data integration layer, standardize the data consumption layer, and enable prescriptive and descriptive reporting with embedded AI and ML models. They will also need multi-skilled roles to handle the end-to-end data-to-insights journey. Due to the demand-supply gap, businesses will need to constantly reskill and upskill in addition to hiring experienced talent from the market.
Tips to succeed as a data analytics professional
With business cycles becoming shorter, data and analytics have become all about speed, innovation, and delivering business value. You need to think about how long it takes for end users to unleash value from the reports or insights you are generating. If it’s not clear or if it’s taking too long, you may be doing something wrong.
Whether you are an aspiring data analytics professional or have been in this field for quite some time, you have the opportunity to not only learn new skills, but also help shape the future.
Here are some tips to guide your long-term success in this area:
Develop expert-level competencies
Data and analytics companies don’t just hire professionals to access specific tools. Any end-to-end D&A projects now typically leverages upto eight technologies and companies are not willing to invest in multiple tool experts anymore. When it comes to cloud, companies use an average of 20+ cloud services and many customers are already using a multi-cloud setup. Building expertise in one or two tools is not going to take you far and building expertise in all is not going to be practical as new services are added every day.
Today, companies are hiring for experts in competencies such as cloud, data engineering, analytics, data management, advisory and service management. These competency experts are individuals who are able to work on multiple technologies in a competency and have the capability to create an end-to-end solution to a real-life data problem.
You should develop a T-shaped or Pi-shaped profile with depth in one (or more) competency in formative years and then diversify to build breadth across competencies for long-term success in your career.
Invest in learning essential technical skills
Data Modeling, Dimensional modeling, and SQL are some of the basic skills a data analytics professional absolutely needs to have. But they are not enough. Go further and consider learning Java, R, or Python programming. Python is among the most common coding languages required in data science and data engineering roles, along with Java.
A data analyst or engineer should be capable of working with unstructured data as well. Seek opportunities to develop your skills on predictive analytics, machine learning, and artificial intelligence to stay relevant. The key is to keep acquiring new skills and tools to stay up-to-date with the latest developments, technologies, and methods that will enable you to deliver the most effective solutions.
Develop your Data Storytelling skills
As a data analytics professional, you should be good at data storytelling. The most important aspect of a data analyst’s job is communicating insights effectively to non-technical audiences, such as the marketing or sales departments. You need to be creative with data to help answer questions or solve problems. You must apply the appropriate data visualization techniques to get your point across and enable your audience to understand the information easily. Whether you are data engineer, data analyst or data scientist, you should develop your ability to present insights in the form of intuitive, information-rich dashboards.
Pursue continuing education
Having a bachelor’s degree in computer science, information technology, or statistics will provide you with the ability to handle and analyze data. However, you may need to pursue post-graduate education to advance your career.
According to KDnuggets, a leading industry resource on data analytics and machine learning, data scientists tend to be well-educated; 88% have at least a Master’s degree and 46% have doctorates. While there are outliers, most data scientists have a sound educational background that is necessary to cope with the demands of this profession. A management degree can also come in handy to help you reach leadership positions faster and excel in it. If you plan to pursue post-graduate education, be sure to work in a company that supports this and will allow you to take classes after work or on weekends.
Focus on your problem-solving and soft skills
Problem solving and collaboration are among the most important soft skills a data analytics professional needs. Problem-solving is an essential aspect of data analysis – it is vital to know what questions to ask. You will get the answers you need if the queries you ask are based on your knowledge of the firm’s business, product, and industry.
A data analytics professional must also know how to collaborate with colleagues and clients. Careful listening skills are essential to understanding what type of data and analyses a client or stakeholder requires. The ability to communicate in a direct, easy-to-understand, and clear manner also goes a long way in advancing your career. In addition, these soft skills can make you more effective at convincing people to act on the findings and help you resolve problems or conflicts.
Data and Analytics Careers at Infocepts
Interested in pursuing a data analytics career in a global company? Apply to Infocepts now
Infocepts was recently named as Great Place to Work and as one of the best firms for data scientists to work for by Analytics India Magazine, alongside some of the biggest names in analytics. We exclusively focus on data and analytics and are known for investing in helping our associates become the best versions of themselves.
Cloud computing is one of the most significant technology transformations since the introduction of the Internet in the early 1990s. Growth in cloud-computing traffic, mobile data traffic, and the continuous development and adoption of AI, IoT, and related technologies also contribute to the rapidly increasing data volume and complexity. After all, data gets created every swipe, click, stream, search, or share. So it is not surprising that there are more cloud engineer jobs than ever.
If you are a cloud professional seeking employment in an increasingly competitive industry, you will want to stand out in a prospective data and analytics firm. Here are five resume writing tips to keep in mind:
- Effectively summarize your cloud skills
Emphasize your accomplishments in cloud-related projects and provide a comprehensive overview of the roles you fulfilled. Prospective employers want to know your daily job responsibilities and how these impacted your company or department’s overall development.
Below is a good example:
‘Highly experienced Cloud Architect with extensive expertise in performing Cloud Readiness Assessments, generating cloud service maps, and overseeing security concerns as part of the wider Cloud Adoption Framework. Strong organizational skills, ability to manage large teams from start to finish, from requirements through design, implementation, and deployment.’
You can also include your professional aspirations to indicate what platforms and services you are looking to build your skills and expertise on.
- Emphasize your technical skills
Highlight your technical skills in cloud computing and data analytics to demonstrate that you are a suitable candidate with the necessary skills for a cloud professional position.
Do you have a solid grasp of the Cloud Service Provider (CSP) market? Highlight the cloud platform(s) you are familiar with, such as AWS, Microsoft Azure, or GCP—the three major public cloud computing platforms. Then, list other cloud platforms you know how to use, such as IBM, Oracle Cloud Infrastructure, or Alibaba Cloud.
Also, be sure to list any experience you may have in:
- Working with various cloud-native services
- Data pipeline design, configuration, implementation, testing, and monitoring
- Database modeling in distributed cloud computing environments
- DevOps and Containerization
- Scripting and programming languages
- Migrating non-cloud native apps into cloud-native architectures and cloud environments
- Processing, managing, and extracting value from big datasets
- Be honest
When describing abilities on your cloud data architect resume, be truthful about your level of expertise. For example, indicate if you are a beginner or if you have mastery of a specific skill.
Understand that cloud skills are still nascent and growing as companies adopt the cloud. Your hiring manager will not expect you to have many years of experience on a particular cloud platform or service. What’s more important is – your ability to demonstrate knowledge and expertise in the skills you claim to have. It would be best if you also showcased the flexibility and confidence to apply your learnings to understand new cloud platforms and services faster.
- Make education a focal point
Your education is as important as your work experience in the data analytics industry. To work as a cloud engineer, you generally need a bachelor’s-level degree. A computer science degree might also improve your employment prospects, with its wide breadth and emphasis on theory.
In addition to your educational qualifications, be sure to list all your training, self-learning, and certifications related to cloud services, infrastructure, or applications. If you have participated in hackathons or innovation challenges hosted by your current organizations or external bodies, list them in your profile – those will make interesting conversations during your interview.
- Don’t make it longer than it needs to be
Your resume needs to impress an employer within 15 seconds. Most recruiters only go through resumes halfway down the page before deciding whether they want to keep reading or not, so you must demonstrate your competence right away. Highlight your:
- Expertise in building well-architected cloud solutions
- Technical skills in cloud services, infrastructure and administration
- Experience in developing cloud systems
- Skills to support cloud operations
Looking for a cloud engineer position? Apply to Infocepts today. Visit our Careers page for more information.
Labelled as the sexiest job of the 21st century by Harvard Business Review back in 2012, data science continues to be an in-demand career choice for many even today. As a leading Data & Analytics firm, our ability to enable our clients to gain disruptive advantage using the power of data is largely attributed to our home-grown talent. This is true for data science as well.
We asked Sanket Ninawe, our data science CoE lead, a few questions to understand his approach to identifying, mentoring and nurturing data scientists at Infocepts. Read on to know more.
What’s your selection criteria for fresh graduates to be part of your data science team?
Many students want to become data scientists fresh out of college and when they come to us, our job is to assess how well they understand data structures, algorithms and programming. As part of the interview process, we review the courses and trainings undertaken by them. We consider students who have undertaken courses like computer science algorithms, statistics, or machine learning in college. We then proceed to test their knowledge on computer science concepts, algorithms, databases and Python.
What happens after selection? Do you conduct any training or orientation for them?
Yes, we put them through an intensive two-month boot camp where our instructors introduce them to various data science concepts and algorithms necessary for building machine learning models. It is an exhaustive training program designed to prepare them for handling a capstone project. As a part of this project, they are required to work on a use case and present their solution to the data science CoE.
Are your trainees project-ready at the end of your bootcamp?
No, they must complete some more add-on trainings such as natural language programming, deep learning, and more concepts. While we train them on these concepts, we also help them understand cloud technology because data science models are typically hosted on the cloud. AWS, Azure, or Google Cloud being some of the popular choices. So, we make them familiar with these platforms over the next few months.
Once we are confident about the progress they have made, we make them a part of our data science CoE. We further evaluate their progress when they become a part of the CoE and provide any additional support they may require. They may then be aligned to a POC or use case we may be working on. It gives the new data science members a chance to gain hands on experience to use their newly gained skills.
What next after their trainings and hands-on PoC engagements?
They begin working as junior data scientists on our client projects, where they will be guided by a senior data scientist to build data models with live data. We closely monitor their performance on the project and when they reach sufficient threshold, we recognize them as qualified data scientists.
After a period of about 3 to 4 years, they become eligible to be regarded as senior data scientists. While on their way up there, they are also expected to help us grow our team. We involve them while conducting interviews including technical rounds. This gives our data scientists an opportunity to explore how data scientists from other organizations work.
How do you ensure that your data science team stays sharp?
We encourage our data scientists to build point of views on latest technologies or new concepts that might be trending. They can spend some time in between projects to learn any new technologies that we may require in the future. The team has the freedom to choose the area of expertise they are most interested in like machine learning, NLP, image recognition, and so on.
In addition, we ensure that our data scientists are confident in communicating externally because our clients are often not well versed with data science concepts. Our team should be able to clearly explain how they have or will build the required data science model. To that effect, we assist them in creating presentations and we also have a monthly forum where they can showcase their work. The forum enables the team to learn from each other while evaluating the rationale for the choices they make to build their data science models. Everything put together helps them to excel in their roles.
Any closing thoughts you would like to share?
Infocepts was recently recognized by Analytics India Magazine as one of the top data science companies to work for in 2022. This was a proud moment for all of us. The average age of my team is 25 years, mainly comprising of fresh graduates and some senior, experienced professionals we have hired. Also, I can proudly say that our retention rate is around 99%.
We are looking to hire both fresh and experienced talent to grow our data science team. You can visit our Careers page to apply.
Thanks to Coronavirus outbreak, we, like many of you, are home bound for the foreseeable future. I figure many professionals will be working from home for an extended period for the first time in their careers and I wanted to share my experience. Over the last four years, I have been mostly Work from Home (WFH). Through this blog, I share my learnings that may be of use to some of you and invite more companies to look at extending their WFH policies to support the requirements of modern workplace!
Having worked in an office set up for 15 years, my WFH experience started with fun during the first few weeks (independence!) to being very lonely after a few months (office sickness anyone?). At one point I even questioned if I was being productive, but now I am a self-certified AWESOME worker from home. I love the flexibility WFH offers and find that my overall effectiveness has gone up. Based on my personal experience, I am sharing a few tips that may help professionals looking to make this transition:
Ambience Matters – Prepare your working space and make yourself comfortable
It took me a while to figure out that WFH for an extended period required a dedicated “working space”. While the visuals of sitting on your “couch” and working through the day may appear appealing, it will not work for long. It is incredible how equipped our workspaces at work are, and so at a minimum:
Set up a secluded room/area with a comfortable chair and desk (standing desk is even better). Working from a couch/ bed is a “no-no” as it compromises both health and productivity!
- Get required gadgets/essentials and arrange them like on your office desk. For me, my office organizer, lap desk, keyboard/mouse, headphone, printer, and other essentials such as various chargers and a globe make me feel at office!
- Set time every day to clean up/organize your desk. Remember, this area (along with your laptop/phone) is where you may be having most of the germs.
Establish Your Routine – Find a pattern that works for you to avoid working long hours
It is very easy to get carried away while working from home – you may end up working double the hours or you may get consumed in a homely matter way beyond you want. The trick is to develop a routine with a defined start, break, and close time – start with something and adjust as needed.
- Pretend as if you are going to the office and complete your morning routine. Starting your day in the pajamas may be very comforting, but you will end up being miserable by end of the day.
- Closing on a set time is something very difficult for knowledge workers. I follow a rule – do not carry the laptop charger outside the office area and leave the office area at a set time every day. It works most of the times and enables me to time box my work hours!
- Take scheduled breaks to do daily chores as well as meeting your family. A 30-45 min lunch break and a couple of 15-minute breaks during office time is standard. The best way to do this is to set reminders or block your calendar!
Set Expectations – Your family and peers will appreciate you
Working from home is not to be mistaken for working for home. Nor should you mistake your family to be working for you. Remember, you are a tenant during the workday. It is best to set the expectations:
- Agree with your family on what they can and cannot do at certain times. You cannot expect 100% silence zone for all of your office hours. I have an agreement with my family to avoid domestic work that causes noise and to avoid conversations (with me) during two pockets of 3 hours each when most of my client and team calls happen. If there are calls outside of those pockets, I can only request.
- A huge advantage of WFH is the flexible work timing. Whatever schedule works for you, communicate with your teams and clients and work out a mutually agreeable chunk of available common/collaboration time. If you have to step out during that block to attend an unscheduled domestic matter, keep them in the loop as you would at work!
- Integrate your work and life creatively. Do some joint activity with your family (they are your colleagues for next few weeks). Help your parents with some domestic work, help your kid with his/her homework and give your partner some space.
Learn Virtual Collaboration – Find ways to reduce the distance between you and your teams
It can be very isolating and frustrating to be working alone. Therefore, it is very important to use latest tools that work in your organization to collaborate and socialize.
- At my organization, we use our internal international bridge for phone calls, Microsoft Teams and Webex for screen share and video calls and the Office 365 suite for collaboration.
- While WFH do not assume other team members know or understand what you are working on. Send out directions/instructions over phone and follow it up with emails/texts. At the start, over communication does not hurt!
- Set aside some time for socializing in a week. Set calls with your friends to know what is happening elsewhere. Yammer, team chats and an intranet are great ways to socialize. Office gossips keep you alive.
Invest in Technology – Avoid unnecessary frustration for you or your team members
The biggest difference when WFH is the frequent meeting requests for phone/video calls you are required to take up. Few things that have helped me:
- Invest in a good headset (preferably wireless and noise cancelling). This is necessary if you think you will be on call for more than a couple of hours every day. It is going to make your life so much easier. For example, I often meet my daily “move goal” during my morning calls where most of the time I am only a listener!
- Revisit your internet connection plans – you need high-speed connectivity to use the modern collaboration tools. Otherwise, you will lose a lot of productive time leading to longer hours.
- Learn the etiquettes of virtual meetings. Simple things such as announcing entry/departure, learning to mute/unmute when you are not talking/talking go a long way in maintaining team productivity and momentum. Remember, your colleague cannot see you (unless you are using video conferencing, which works great!), so learn to receive and give voice cues in-lieu of visual ones.
Integrate Work and Life – Take advantage of your flexibility
WFH offers unique opportunities to integrate work and life. May be you are stuck solving a problem – take a break and recharge; or, perhaps you never got an opportunity to interact with your kids during the day, now is your chance! Here are a few things that helped me:
- Pending local situation, set time to go out at least once out of your home, for a walk or whatever pleases you
- Give yourself projects for your free time during office hours and beyond that – it could be self-training, reading a book, investing additional time in your hobby, organize/clean up your wardrobe, or learn some new recipes!
- Exercise, keep control on your taste buds, and do not turn your kitchen into a 24-hour pantry!
- Take rest, do not binge watch latest favorite series, rather catch up on your lost sleep
As I reflect back, I realize it is an important art/skill in our professional career to be able to collaborate without being collocated, being productive without being monitored! Moving forward, if a company does not offer flexible WFH options, they are likely to lose the talent acquisition and retention game. While most companies associate this need from the millennials, I anticipate this becoming an expectation from all generations given the “forced” WFH experience due to the COVID-19 circumstances. Over the next few weeks, I hope most of us engage in a self-learning course in self-discipline and managing oneself to successfully WFH!