3 Data Engineering Solutions To Attrition For Small Teams!

In these last few years, we’ve seen a combination of employee turnover while job openings soar to record levels. Shifting the balance of power to the worker who is now prioritizing a better work-life balance over their careers. Many of the top jobs now offer flexible work arrangements at six-figure salaries.

 

This makes it harder for employers, especially in the SaaS industry to maintain fully staffed developer teams. Causing incomplete projects, inefficient data, and many other issues.

 

Here are 3 Solutions to dealing with attrition for small data engineering teams.

 

Problem #1: Extremely High Cost of hiring a full data team

It is no secret that in recent years, the SaaS industry has become extremely competitive. The top positions, according to the recent top 10 best jobs in America for 2022 by Glassdoor, all have a median salary above six figures. 

 

A full data team could consist of a Developer, Data Analyst, Data Engineer, Data Scientist, and Data Architect, as each title brings its own skillset. This small team may cost a million in salary, not calculating training, bonuses, and salary increase. This creates a shortage of skilled workforce since salaries must be competitive to attract quality employees. 

 

Solution #1: Hire a Full Team of Data Expert at a lower cost

In summary, the problems we’re trying to solve here are:

  • The high cost of hiring and maintaining a full team of multiple data experts
  • Attrition causes a massive amount of knowledge to leave your company
  • Inefficient Data projects due to employee turnover

 

What if you could hire a full team of experienced & skilled experts at a lower cost than one full-time employee? 

 

Nom Nom Data makes it possible for companies to access a full data team of experienced & skilled experts without the cost & overhead of managing people, or the risk of employee turnover.

 

 

 

A total of over 150,000 active open positions, communicates how many employers are searching for experts. The way that most are mitigating the shortage of data experts, has been to hire additional junior developers to fill these positions, just in case someone leaves. 

 

This brings us to the second problem…

 

Problem #2: Lack of Experienced & Skilled Employees

Organizations both large and small experience data issues that delay their project timelines. Having data experts on your team can easily be the difference between a smooth work week and putting out fires every day. 

 

These issues occur when people leave the company halfway through working on a project. The lack of documentation creates issues almost weekly to which managers oftentimes don’t understand why. This is very common. Due to attrition, companies have a massive amount of knowledge that goes out the door. 

 

Unraveling The Tangled Web We Wove they call it.

 

Managing these projects can easily become too complex to figure out, too intrusive to have to change everything, and very expensive. The symptoms can be described as:

  • Slow time to resolution of errors
  • Fragile processes that break often
  • Inability to add new information quickly
  • The requirement to hire expensive experts

 

The ideal outcome is one where managing your data is easier, self-serviced, and self-documenting. A way to create an automated system architecture that processes your data efficiently, while self-documenting to minimize the effects of churn.

 

The pandemic has really emphasized the value of data because of how sharp and unpredictable the changes from this crisis have been – more companies are turning to real-time data to understand what’s going on and make decisions.” Daniel Zhao, a senior economist and data scientist at Glassdoor

 

Solution #2: Hire Long Term Data Experts

With most companies turning to data, the solution we’re looking for are:

  • Experienced Data Experts with the skills to make managing data easier, self-serviced, and self-documenting
  • Long term data managers who make your projects efficient and stable
  • Data Experts who can avoid the 70% failure rate

 

 

 

Find out how Nom Nom Data is able to finish projects so quickly…

 

Problem #3: Time To Complete Projects Too Long

When it comes to SaaS projects, we all know that the longer your projects take to complete, the more expensive they become. Three main causes of data projects taking long are:

 

  1. The time it takes for employee onboarding

Finding employees can take a while as you may need some time to sift through candidates. In addition to the whole onboarding process, which includes their future start date and employee training, reading through the documentation may add more time than desired to get started on the project.

 

  1. The ability and expertise of your Data Engineering team

Companies will ofter hire junior developers, sometimes right out of college to work on their projects. This helps the company by lowering workload and payroll while also filling open positions, but could be a big reason for longer project completion times.

 

  1. The process put in place by the company

We found, that companies often don’t have the right processes, tools, platforms, and frameworks available to help the employees become more efficient. It’s not uncommon for people to leave the company halfway through a project. Leaving the new employee to struggle through the documentation, or try to start the whole thing from scratch. Making the delay time even longer.

 

Solution #3: 

The missing piece is the Nom Nom Data platform & framework, the Nominode. People in this climate are too slow and don’t have the right tools for ramping up in a remote environment. With it, we’re able to finish projects in as little as 3-6 weeks. Whereas others may take 10 months or more.

 

Even if you put 4-5 people on the project they couldn’t replicate our Nominode. And you run a high risk of not completing the project, since the people may not stick around for long enough. This gives you the advantage to scale quickly with a platform that makes managing your data easier, self-serviced, and self-documenting.

 

 

 

If you’re struggling with your data projects or failing to understand why – Schedule your consulting call with Nom Nom Data.

 

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