The Art of Working with Data Engineers as a Data Scientist

How data engineers and data scientists can collaborate effectively in the present world of big data

Data science is no longer a strange term. Data Science along with big data, machine learning, and artificial intelligence are the technologies that drive a vital change in the world and create extensive job opportunities for data engineers and data scientists.

Big Data has changed the face of the world. The term Data Science refers to the "study of data," this concept describes a way to gather up all the information available on the world wide web and analyze these to help organizations make informed decisions. With 2,3 trillion gigabytes of data created each day, companies come across a broad range of information on their users, market, and much more which allows them to improve their product/service constantly. The market has understood the opportunity that Big Data shaped and the mounting demand for data engineer and data scientist jobs reveals this.

Moreover, in 2011, Harvard Business Review specified Data Scientists as the sexiest job of the 21st century to point up the prosperity of the profession! However, this job field seems not to be fully mature till now and continues to be subject to misunderstandings. It seems for many a blurry technical 'thing' that could potentially implement their product or service. This concept could fail in the good use of resources. Coming back to the fundamentals of these professions and decrypting the value of each. Working well together as a data scientist or data engineer one may have found it challenging work. As a data scientist, you're being provided data that doesn't match the expectations and as a data engineer, you're asked to work on tasks that are easier said than done. Here are some ways discussed that help data scientists and data engineers work together more effectively.

Provide Context

As a data scientist, the work is associated with stakeholders to understand the context of the request, determine the priority, and agree on a deliverable. The same approach can be used when requesting a data engineer. Data engineers are constantly overloaded with requests to pull new data or investigate data pipelines or quality issues. Their urgency is to understand the context of the request to prioritize accordingly against their backlog of tasks.

Guidelines for  Data scientist:

Answer the 4 W's while making data engineering requests.

Who benefits from this data? — Marketing urge to know where the website visitors are landing from.

What data is needed? — Website visits to all marketing-owned pages.

Why is this data needed? — Recognizing the source of the website visitors helps marketers optimize their exercise to prioritize high-converting channels to drive more sales.

When is this data needed? — Try to make requests in advance rather than late to get on the data engineering backlog. Additional time should be allocated to the marketing deliverable date to account for data engineering pulling the data.

Guidelines for Data engineer:

A help page should be created listing the details needed for a request and review with the data science team. Alternatively, this information can be asked in the data engineering request form. This preserves both teams' time to have the details upfront without having to go back and forth with questions.

Data scientists may not be updated with all the data available. Creating a data catalog with location and descriptions can be considered for data scientists to analyze before requesting data that may already exist.

Provide Data Specifications

Data engineers encourage requests with clear specifications because they can't be expected to know what's best for the analysis. As a data scientist clarity on the data, and fields is a must, and any data handling logic such as dealing with null values, and the date range is required for data.

Role of data scientist:

They should provide full details when making a request. This reduces the back-and-forth questions meaning the request can be done shortly.

Sample data should be asked containing a few days of data to review values and confirm all essential fields are at hand before data engineering pay out time pulling the history, especially if you need years of data backfilled.

They need to be specific with how data should be updated to avoid duplicate records.

Role of data engineer:

A checklist of items should be created for standard requests such as pulling in new data to review against the request details to confirm all the information needed.

This can escape delays when you get to the request and realize you need more information before you can begin.

Provide QA Specifications

Data engineers can assist up to hundreds of data pipelines and the main part of their job is to make sure these ETL jobs run error-free and troubleshoot those that don't. It helps in reducing the turnaround time for the request by providing QA checks to run before data is passed to you for review.

Data scientist advice:

They provide assistance on the expected values or SQL statements the data engineer can run to confirm the ETL is running as desired. The more details provided, the fewer questions will have to answer.

Data engineer advice:

Take a quick look at the data loaded to see if anything looks strange. For example, if every column is null that's an issue that needs investigation. They should ask the data scientist if any standard checks can run to confirm the data is loaded as expected.

Final Thoughts

As Data Science big data and machine learning continue countless Data jobs evolve, data scientists and data engineers are expected to learn how to work together effectively to be successful in the organization. While it may seem difficult to collaborate in perfect harmony now, in near future may bring us one step closer to working together effectively.

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