Behind the Scenes: The Unspoken Challenges of Data Engineering
Intrigued by the world of data engineering and considering a career in this exciting field? You are very welcome.
But, as you embark on this journey, it is important to understand the challenges that data engineers face, especially the ones who are starting their careers in data engineering. In this article, I will not be focusing on the technical challenges; instead, I will explore the ones that you can only truly experience on the job.
1. A lack of recognition
You will often work behind the scenes, ensuring data pipelines are robust, scalable, and reliable. While data scientists, analysts, and Power BI developers receive recognition for their insights and visualizations, data engineers often go unnoticed. This may feel like a punch in the gut considering your data powers the models and dashboards, but this is where a good manager is required to step in and ensure their data team gets recognized for keeping the lights on data availability and data quality.
2. Dealing with Ad-Hoc Requests
You will frequently be bombarded with ad-hoc requests from various stakeholders within an organization. They are a part of the job, but they can disrupt your planned work and require quick turnarounds. These requests can range from extracting specific data sets to troubleshooting issues in data pipelines.
Can this stop?
No.
Can we do anything about it?
Yes. As a Data engineer, you need to be adaptable and prioritize tasks effectively to meet the needs of the business while maintaining the stability of the data infrastructure.
3. Getting something done “asap”
Ah yes, get this data from here to there and that should be it. Piece of cake, right?
Business users often have high expectations and tight deadlines for data engineering projects even though they may not fully understand the complexities involved in building and maintaining data pipelines. Data engineers face the challenge of managing these expectations and educating stakeholders about the time and effort required for quality data engineering work. Balancing the need for speed with ensuring data accuracy and reliability is a constant juggling act.
This is where a data engineer’s communication and expectation management are tested. When faced with tight deadlines, it is crucial to communicate with stakeholders and have an honest conversation about the time and effort required to deliver high-quality work. Help them understand the complexities involved in building and maintaining data pipelines. By setting realistic expectations, you can alleviate some of the pressure and ensure a better understanding of the process.
When faced with tight deadlines, work prioritization and constant updates are another set of crucial skills that separates a good data engineer from the one who is drowning in work:
- Identify the critical tasks that need immediate attention and focus on those first.
- Break down larger projects into smaller, manageable tasks and create a timeline to ensure that you are making progress.
- Do not forget to keep stakeholders informed about your progress and any challenges you may encounter along the way. If you anticipate delays or roadblocks, communicate them early and propose alternative solutions or adjustments to the timeline.
4. Data Reconciliation
One of the lesser-known responsibilities of data engineers is the task of data reconciliation. Data reconciliation involves comparing and validating data across multiple sources or systems to ensure consistency and accuracy. This process is crucial for identifying discrepancies, resolving data quality issues, and maintaining data integrity.
Data engineers are often called upon to reconcile data when inconsistencies or discrepancies arise. This can be a time-consuming and complex task, requiring a deep understanding of the data sources, transformations, and business rules. Here, you must meticulously analyze and compare data sets, identify anomalies, and work towards resolving any discrepancies.
Can it be challenging?
Absolutely.
Data reconciliation can quickly turn from a simple update to a complex time-consuming process due to various factors, such as differences in data formats, data quality issues, or changes in data structures over time. It requires a keen eye for detail, analytical skills, and the ability to navigate through large volumes of data efficiently.
5. The Constant Learning Curve
In our world, the only constant is change. New technologies, tools, and techniques emerge at a rapid pace, requiring you to continuously upskill and adapt. It may seem overwhelming, but it is also an opportunity for growth. You are not expected to know all the tools, but you are expected to know just enough to perform in your role. A good thing about data engineering is that you have an abundance of material to read and practice available over the internet.
Conclusion:
In conclusion, data engineering is a challenging yet essential field that often goes unnoticed. From managing ad-hoc requests to reconciling data and collaborating with various teams, data engineers play a crucial role in ensuring the smooth flow of data. You are the backbone of the data-driven revolution, and your contributions should be celebrated. Keep powering those models and dashboards and know that your dedication and expertise make it all possible.
So, the next time you see a beautifully visualized dashboard or gain valuable insights from data, remember that behind it all, there is a dedicated data engineer making it possible.
