Erik Quijano, MBA’s Post

If you hire a data scientist before you've hired a data engineer, congratulations, you've just hired an entry-level data engineer!* *Unless they've had the same thing happen to them multiple times, in which case, congratulations, you've hired a seasoned data engineer. #datascience #dataengineering

#Hiring #Junior Data Scientists. comment below for JD

We are currently #hiring a Data team for a dynamic start-up 🤩 Why the current employees love working at this company? "I'm encouraged to have a voice and share my opinions." "I spend my days solving big tech problems - efficiently and intelligently." "Data privacy and protection is at the heart of what we do." Why we are excited to recruit for this organisation? • Building team foundations that will positively impact the organisation and people's careers • Building a technical team and being part of their success story via placing great people • We can feel the energy, momentum and motivation whenever we catch up with the team You can find the job adverts below: • Machine Learning Engineer - https://ow.ly/7oo450Qt3jf • Data Engineer - https://lnkd.in/gy-XTrh5 • Data Scientist - https://lnkd.in/gd4g59Pp We are also on the look out for Data Architects and Data Analysts, position descriptions coming soon. #watchthisspace Please share this post with your Data network 😊 #datascience #machinelearning #ml #ai #data #dataarchitect #dataanalyst #dataengineer #aws

After you establish the right data leadership (full-time or fractional), your first hire should be a data engineer. 👨💻 If you hire a data analyst first, they'll need to be a data engineer first to build the data infrastructure to support analytics. 👷♀️ If you hire a data architect first, they'll need to understand all the company's systems, determine what platform/tools to use, and likely be a data engineer first to build the data infrastructure and then refine it accordingly. 👩🔬 If you hire a data scientist first, you can speak to any of the 90% of data scientists turned engineers over the last 5 years because they couldn't build appropriate models with the trash (or non-existent) data infrastructure they were handed. Find a data engineer with a solid background in designing and architecting data platforms, and then you can hire the architects, analysts, and scientists afterward. #EGDataGuy #datateam #dataengineering

I think this is mistake. Start with business/data analyst with domain expertise and focus on driving value for the business. Start with most important and impactful use cases and go from there. Do a proper modelling. Climb the data maturity ladder as you become more able to kick off advanced use cases. Get tech specialists afterwards when you need to super optimize particular aspects. There are several platforms on the market that can help any company to leapfrog this initial buildup period that is just sunken costs and let you focus on the results. I have seen too make teams just digging into building own stuff for the sake of building with intangible business value but significant costs incurred.

This thought came to my mind after being data engineer for the first 3 years. After knowing the company’s business and all, it is all about business in the first place. Basically, in my opinion, if the data is still in small size, having data analyst that can do a little job of data engineer is better than having data engineer. In the end, having the basic team that can help business grows is much better. Then you can build the supports after.

I can't agree enough with this... A lot of Data Analysts/Scientists are becoming Data Engineers, not because they originally wanted to, but because the infrastructure they work with is so bad that they just have to try to get the skills to build one. Secondly, I think this is another good reason we now have ELT and Analytics Engineering as against ETL and Data Engineering. #dataengineering #analyticsengineering #etltools #elt

This is a solid piece of advice. Read it and heed it. #dataengineering #datateam #datastrategy #datarogue