Have you ever imagined a day in the life of a big data engineer? Working with computers and numbers every day sounds boring. However, it’s the passion of every big data engineer, and I’m not an exception.
So today, I will share my duties when working in this position in this guide. Let’s jump into the details! You may get inspired then.
Who Is A Big Data Engineer?
Big data refers to huge sets of information that companies gather during their operations. And as a big data engineer, I help them manage those data volumes.
My main job is to design and build software systems. After that, they will act as a foundation to collect and process the data.
I’m also responsible for creating data architectures that fit my company’s needs. To do this, I use different programming languages and tools. The best option for my job depends on the specific project.
Another critical task for any big data engineer is to mine data from different sources. Then, they create efficient models for companies.
Handling a whole data system is complicated because the process involves multiple steps. That’s why I work closely with data scientists and data analysts to achieve the best result.
A Day In The Life Of A Big Data Engineer
I stay busy throughout the day because there are always tasks to complete. Now, I will walk you through my typical day to help you picture what keeps me busy.
Morning Routine
My work day begins at 9 am as a full-time big data engineer. However, your schedule will be more flexible if you want to become a freelancer.
I often have breakfast on the way to work. It can be hamburgers and a cup of coffee to help me set the tone for the busy day ahead. Then, I settle down at my desk and dive into my work.
Checking Emails
One of the first things to do is check my emails. Staying on top of any changes or new tasks that may have come up overnight is crucial.
Reviewing Any Updates Or Requests
Once I’ve gone through my emails, I take the time to review any updates from my team and clients. The updates are often relevant to new data requirements or urgent analysis needs. Understanding these updates helps me prioritize my tasks and ensures I’m aligned with the project goals.
Data Loads
A significant part of my role involves handling data loads. I categorize them into two types: one-time loads for ad hoc projects and recurring extractions for regular reports.
For ad hoc projects, I analyze the specific data needs, design the data extraction process, and load the required data onto the sandbox environment. This approach allows me to explore the data effectively.
On the other hand, recurring extractions are often about setting up automated processes. I can extract and load data at specified intervals based on those foundations.
Data Preparation
After loading the data onto the sandbox, I will work on data preparation. This task involves transforming the data into a usable form for analysis.
I engage in side-by-side wrangling, coding, labeling, and creating metadata to handle this process. These actions help me clean and structure the data effectively.
SQL and Python are also beneficial as I use them to perform various operations on the data. I spend a lot of time on this part as it lays the foundation for accurate analysis.
Data Pipelines
Another vital aspect of a big data engineer is creating data pipelines and exposing data for analysis. SQL is my best assistant for these tasks.
For example, I can use SQL to design and implement data pipelines. It’s also helpful for transforming raw data into organized structures.
Optimizing Queries
Now I have the pipelines. Scientists and analysts will use them to improve datasets for research. But my task doesn’t end there.
To optimize the queries, I have to divide and organize the main tables properly, then store them in separate databases for easy access when querying.
Automating And Publishing Analysis
Another exciting aspect of my job is automating and publishing analysis results. After conducting the analysis, I transform the results into interactive web applications.
I also turn prototype scripts of machine learning and analytics into regular processes. The goal is to make them run smoothly and automatically.
End-Of-Day Review
Before leaving, I review my progress and make sure everything is on track. It often takes me a while to reflect on the tasks I completed. And if there is some time left, I can prepare for the challenges that await me tomorrow.
Tips for Future Big Data Engineer
If you want to become a big data engineer, you’ll need to focus on education first. I recommend pursuing a bachelor’s or master’s degree in computer science, statistics, or business data analytics. They will give you a solid foundation in the necessary technical skills.
Gaining experience is crucial. You can consider freelancing or interning. Working in related positions also helps you earn hands-on experience.
Think about getting professional certifications, too. Certificates like CCP or CBDP are valuable credentials to have.
Remember, continuous learning is essential in this field. So, stay updated on the latest technologies used in big data engineering. Otherwise, this ever-evolving industry may leave you behind.
Conclusion
My workday is filled with challenges and excitement as a big data engineer. But my passion for engineering and high salary encouraged me to continue this journey.
Hopefully, this guide has given you a thorough insight into the world of big data engineering. If you want to pursue this career path, keep exploring and learning. Then, you can push all the boundaries.
Thank you for reading!