07 Jul 2022

A Typical Day In The Life Of A Data Engineer | Ekeeda

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A Typical Day In The Life Of A Data Engineer

Being a Data Engineer is one of the most challenging roles in the industry. You need to possess skills to extract data from various data sources, transform it into the staging area, and load it into the data warehouse system. The process is termed ETL – which means Extract, Transform, Load. Basically, you need to prepare and transform data with the help of pipelines. For all this, you need in-depth SQL knowledge and other database solutions like Google Cloud Bigtable, Hadoop, and Cassandra.

Now let’s start with my introduction:

Hi, I am Neeraj Shrivastav from Delhi. After completing my Master’s in Computer Engineering from VJTI, Mumbai I did work for over a decade in a lot of units in IT and software-related processes and industry. Over the years, I’ve been assigned various roles like software engineer, project engineer, project manager, business analyst, and product engineer. One and half years ago, I felt the industry is at a paradigm shift, especially in this era of data-driven decisions, so I wanted to make a career move. As I was looking for future career opportunities, I came across the AI Data Science ML field So, I started to delve into it through various data science online courses, and thus, my passion for data and data engineering was suddenly developed. After doing a lot of research I came up with the Data Science Online training program I wish to opt for. The course curriculum was industry-oriented with the latest technical trends, it was taught by data professionals from fortune 500 companies and the capstone really helped me gain industry-level knowledge and boost my confidence. In just six months I was able to deliver results and become a certified data scientist with pride.

The placement management team was very supportive and helped me grab opportunities in various tech and start-ups. Finally, after giving a couple of interviews, I was able to secure a job as a data engineer at one of the most reputed financial services in India. I’m thrilled with it and pursuing my dream data as a data engineer. Now, let’s take a look at What is Data Engineering and What a Data Engineer does in detail: 

Introduction To Data Engineering

Data engineering is the practice of designing and building systems to collect, store, and analyze data on a large scale. It’s a broad field with applications in nearly every form of industry such as healthcare, finance, shipping and logistics, research and development, etc. Today, floats everywhere and companies can collect massive amounts of data, for this, they will need the right people and technology to make sure it’s valuable and useful by the time it is filtered and reaches Data Scientists and Data Analysts. It’s a complex task of making raw data usable to data scientists and other groups in the company. The data engineering field encompasses numerous specialties of data science.

What Does A Data Engineer Do?

Data Engineers create raw data analysis to come up with predictive models and show the latest trends for the short or long term. Data Engineer builds the systems and pipelines to collect, manage and convert the raw data into a valuable form of data for business operations, making it ready for standard access and interpretation.

Being a data engineer, your ultimate goal is to make data accessible to companies, managers, data scientists, and clients to use it to evaluate businesses and optimize their performances. It’s impossible to make sense of massive data being floating around in the industry without a skilled and brilliant – Data Engineer.

Some common tasks data engineers have to perform:

  • To acquire datasets that will align with business goals and requirements
  • To develop algorithms to transform data into valuable and actionable insights
  • To build, test, and maintain the database pipeline architectures
  • To collaborate with management to understand business objectives
  • To create new data validation techniques and data analysis tools
  • To make sure you comply with data governance and security policies.

When you work for small or medium-scale firms, you often take a lot of data-related tasks and different variety roles. Some big companies will also have data engineers solely dedicated to building data pipelines and others focus to manage data warehouses – both populate warehouses with data and create table schemas to track where data gets stored along with privacy policy.

Data Engineer Skills Required

Learn the fundamentals of coding skills, cloud computing, and database design to take a headstart in your data science career: 

Coding -  Proficiency in coding languages will be important for this role, so you should consider taking up data science online courses that will help you learn and practice your skills. Common programming languages include Java, SQL, NoSQL, Python, R, and Scala. Of these, SQL is very important to learn when it comes to data handling. Every day SQL is in use to explore data and table manipulation. 

Note: Python & SQL are a must for interviews and daily work activities. The rest are good for knowledge; why I am saying this is the more knowledge you have the better it would be in this vast and complex data industry.

Relational & Non-Relation Databases - Databases are mostly common solutions for data storage; so you should be well-versed with both relational/non-relational databases form and how its carried out. 

ETL - ETL stands for Extract, Transform, and Load systems. It is the process by which you can move data from databases and other sources into a single repository such as a data warehouse. Common ETL tools include SAS, Singer, Hadoop, AWS Glue, Stitch, Google Cloud Dataflow, Oracle Data Integrator, etc.

Data Storage - Data is huge and not all types of data could be stored in the same way. As you design data solutions for the company, for instance, you will have to know when to use a data lake vs a data warehouse. 

Automation and Scripting - Automation is a necessary part of your work with big data because companies are able to collect a lot of information. So, you should be able to write scripts to automate repetitive tasks.

Machine Learning -  Machine learning helps to get a good hold of the basic concepts to better understand the data scientists in your team.

Big Data Tools - Data Engineers work on huge and complex data. They are given tasks to manage big data. Therefore, you need to be equipped with tools like Hadoop, MongoDB, Numpy, Pandas, and more.

Cloud Computing - You cannot store big data in one single computer or pen drive. You’ll need to understand the cloud storage structure and cloud computing because companies trade physical servers for cloud services. As a beginner, you may consider a course in Amazon Web Services (AWS) or Google Cloud. I’ve stated just for examples but knowing any cloud platform is useful, such as any open source. Apache Airflow will help to establish automation in the pipelines.

Data Security - Although some companies may have dedicated data security teams, data engineers are still asked to securely manage and store data to protect it from loss or theft.

Soft Skills -  Mere technical skills will land you only a job, but soft skills will let you co-ordinate with teams, show performance and boost your growth. Honing your verbal and written communication skills is of utmost importance. Most of the time you’ll need to translate technical information to business language or vice versa to easily interpret with the stakeholders.

As a Data Engineer, you will work around the massive amount of data in a day’s time. If you’re unable to translate the data for business motivation, it will be difficult to produce a good analysis of it. Hence, the need for skill development.

Business change is constant, there has to be adaptability to it. You should work with various teams so there has to be team bonding, collaboration, good communication, and convenience skills.

Data Science And Engineering Career Path

Data Engineer's job isn’t always an entry-level role. Instead, many data engineers start their careers as software engineers or BI (Business Intelligence) analysts. As you advance in your career, you will move into managerial roles or become a data architect, solutions architect, or ML engineer.

Now let’s see how will be the interview process:

Usually, any interview process will be around 3 to 4 rounds.

  • After a call from the recruiters, you will have to take home or online technical round that will test your SQL and Python skills. This will be a time-based test, so the more you can solve, the more you will score and the better the chances for selection into the next round.
  • For Python, your knowledge of data structures and algorithms will be put to test; not as much as a software engineer. Recruiters will ask questions of libraries like Numpy, Keras, Skearn, error handling, stats for ML, or lambda function. For SQL, questions will be based on Window functions, aggregate functions, RDBMS, joins, and more. There would be 2 or 3 rounds to test your data modelling, data extraction-transform-loading system, and more. 
  • It goes without saying communication skills also be tested wherein how well you can transcribe the complex piece of information to the stakeholders. How will you handle high-priority work, how will you communicate with your team, etc.

Husshh! That was a hell lot of Python, ML, and SQL Data engineer interview questions and tuff interview round. Relax! The best way to prepare for such technical interviews is to take up the capstone projects offered on various platforms like Ekeeda Data Science online training. These capstones give you a complete sneak peek into the real industry and accordingly prepare and boost your confidence to speak at the time of the interview. Now let’s discuss what I intend to say in my blog:

What's A Typical Day In The Life Of A Data Engineer?

Now that you know what’s data engineering? What does a data engineer do? Skills for data engineer? During the interview process, aren’t you curious to know how a data engineer spends his or her day? Your days as a data engineer will mostly consist of data collection, and meetings with stakeholders to meet the business objectives. Then we gather the gather and later explore and assess it.

In a typical day’s time you’re expected for:

Define The Data Model 

In this process, we will build the ER diagrams (Entity-Relationship). We have to provide the conceptual design of the data objects, and the association between the data objects & the rules. A conceptual design of how the tables will be structured is required so that we will ensure that the data design meets the business requirements of the stakeholders.

ETL Process

Throughout the day we run the extract-transform-load process; wherein you will have required coding skills in SQL for the tables and Python is required to create pipelines. ETL is all about data extraction, transformation, and data loading where required. Later we need to create pipelines and push the queries onto the dashboard to present it with the business objectives and conceptions.

Challenges I Came Across While Searching For A Job

Since I was making a major career transition in data science the move was quite challenging. 

Here are the challenges I faced and how did I overcome them: 

  • I didn’t work on SQL for a long time. That’s why I couldn’t impress recruiters, and I needed a revision. I took Ekeeda Data Science Program to help with it. This course helped me in understanding the complete ETL design, pipeline, and data modelling and gave a hands-on practice in SQL coding skills.
  • I had no intense experience in data engineering projects. Ekeeda helped me to get the much need experience and I was able to work on a few capstone projects with them.  Data Engineering Portfolio – I worked on a few personal projects to hone my skills and was able to build my profile on GitHub.

On A Concluding Note -

It took me around 6-8 months to study, prepare, job search, give an interviewer, and finally land my dream job. 
SQL and Python are the most important skill to master. Communication and knowing business goals and objectives are also very important soft skills.
Lastly, work on as many projects as you can to implement all your data engineering concepts at work!

Get Certified To Secure The Best Data Science Jobs

A certification can let you validate the skills you’ve achieved to the potential recruiters. Therefore, preparing and getting certified is an excellent way to develop your skills and knowledge. Options include Ekeeda Data Science Program, IBM Certified Data Engineer, Google Cloud Certified Professional Data Engineer, etc., and more. During your spare time, check out a few job listings for the roles you may want to apply for. Try and check what certification it requires and see if it might be a good place to start. I would recommend building a portfolio of data engineering projects.

A portfolio is often a key component in a job search, as it will let recruiters, and companies see what you can do. You can add the data engineering projects you’ve done independently or as a part of the course to a portfolio site. Alternatively, post your work to the projects section of your Linked profile or a site like GitHub – both are free versions for uploaded portfolios.

Take a start with an entry-level position. Many data engineers start with entry-level roles, such as BI analyst or database administrator. As you gain experience, you will pick new and advanced skills to qualify for more advanced data engineer or data science roles.

Platforms like Ekeeda offer Data Science Program that will form an excellent road for starting your journey as a data engineer and secure a role in top tech companies and startups. 1:1 live classes conducted by expert data practitioners and an industry-related curriculum will introduce you to current technologies and techniques in data science.

Take a look at the best data science program here!

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