How do you learn and update your data quality skills and knowledge as a data engineer?

A data engineer, or anyone working in the tech industry, needs to constantly update their knowledge and skills. You can use the following method to improve your data quality abilities and knowledge: Blogs , forums, Online Courses ,Tutorials, Online Communities. Above all having a learning mindset and with proper goals like getting a certification in next 90 days. A certification will give you a "driving license" to work in a new tech.

Data Quality: "is this data what I'm expecting to see". The hard part about assessing quality is that many will have differing opinions. Sometimes the data is "correct" when people think it is "incorrect". Other times the data is "incorrect" when people think it is "correct". Both of these situations present problems and can be overcome by talking to more people that use this data. The simplest way to be better at understanding data is to use it with the perspective of the end-user. Ask if you would be able to use this data. If you have access, ask someone else who uses this data where the inconsistencies lie - they will certainly be able to tell you what needs fixing.

Understanding data quality dimensions is crucial for a data engineers. Data quality dimensions refer to various aspects or characteristics of data that determine its overall quality. Common dimensions include: 1- Accuracy: Ensuring data is correct and free from errors. 2- Completeness: Confirming that all required data is present. 3- Consistency: Verifying that data is uniform across different databases or sources. 4- Timeliness: Ensuring data is up-to-date and relevant for the intended purpose. 5- Relevance: Confirming that the data is applicable and meaningful for the task at hand. 6- Validity : Checking that data conforms to specified formats or rules.

In my experience, it’s important to remember what these tools and frameworks are accomplishing for us. Frequently as a Data Engineer, it’s not possible to be a subject matter expert on the data for every pipeline we write. So our efforts should empower the people who are best placed to assess the quality of the data. Tools and frameworks should help us bring automation to Data Quality in a way that allows subject matter experts, who may not be as savvy with Python, SQL, Spark, to get closer to directly influencing the rules that run as part of our automated data quality checks. Otherwise, as Data Engineers, our ability to encode plain quality rules as code becomes a bottleneck for our organisation’s Data Quality initiative.

Something I've found helpful, at least insofar as it's possible, is to work with your management team to set aside time for routine upskilling. No matter what form it takes; attending that webinar on a new potential platform you can quickly migrate to, or a short course that teaches you how to leverage your existing stack's most recent improvements, or even attending a conference/event where you can align yourself to what the best of the best are up to - the market is flooded with tools, and ensuring you're setting yourself and your company up for success requires a good deal of effort. Since that effort translates to business value it should be included in your active, and compensated, responsibility set!