Alexandre Momeni’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

This post made me smile this morning! Most business leaders understand sales and marketing, since they usually have that background. With everything becoming “data-driven” a resource grab for data talent is ongoing. However, most business leaders have no idea how nuanced the data field actually is and “data scientist” is the sexiest title most people think of. (Well maybe not this week, AI engineer is taking the stage….which is still data dependent, btw). Hence, the painfully truthful post below. If you have data that is not where it needs to be or the shape it needs to be in, you need to start with data engineers. When the data starts to flow and has meaning, then a data scientist can work their magic. Foundation first, data application second, insights later. #digitaltransformation #datastrategy #dataengineering #digitalbusiness

#hiring Data Scientist, Sheffield, United Kingdom, £56K, fulltime #jobs #jobseekers #careers £56K #Sheffieldjobs #Yorkshirejobs #ITCommunications #datascience #dataanalytics #machinelearning #bigdata #dataengineer Apply: https://lnkd.in/gDjJn9Ey DWP. Digital with Purpose. Can you apply data science to help us design and deliver better public services and transform how UK government works? Can you use a variety of data handling tools to harness the power of data

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

This happened to me at 3 out of my 4 jobs in data. As such, I've become an excellent data engineer without ever having the title. #dataengineering #datascience #data #datascientists #dataengineers

in a nutshell 😂

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 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.

How simple and correct this is! To leverage on the importance of having the data engineer first: 📌Foundation of Data Infrastructure: Data engineers build and maintain the foundational infrastructure required for collecting, storing, and processing data. This is critical for any AI initiatives as high-quality, well-organized data is essential for training accurate machine learning models. 📌Data Quality and Consistency: Data engineers ensure that data is clean, consistent, and reliable. Without this, any insights or models developed from the data could be flawed, leading to inaccurate conclusions or ineffective AI solutions. 📌Scalability: As organizations grow, the data they handle also scales. Data engineers are instrumental in designing systems that can scale efficiently, ensuring that as more data is collected, it can be handled without performance bottlenecks. 📌Cross-Departmental Collaboration: Data engineers often serve as a bridge between various departments within an organization, facilitating data integration and flow, which is crucial for a holistic approach to AI. 📌Regulatory Compliance: With the increasing importance of data privacy and protection, data engineers help organizations comply with regulations like GDPR or CCPA by implementing proper data governance. 📌Innovation and Competitive Edge: Having a well-structured data engineering process allows organizations to quickly experiment with new AI technologies and stay ahead in the market. 📌Cost Efficiency: Proper data engineering can help avoid costly mistakes and inefficiencies by ensuring that data systems are optimized and well-maintained. 📌Data-Driven Culture: Data engineers are at the heart of cultivating a data-driven culture within an organization, enabling other roles to leverage data effectively for decision-making. #dataengineering

Once is an accident, twice is a coincidence, and three times is a pattern. Last 4 weeks: 1. "We're looking for a mid-level data engineer. Do you know anyone that's looking?" 2. "We're thinking of adding the first data engineer to the team. Can you help with the profile and recommend anyone?" 3. "I've been at this place for a few months but would prefer something else. Know anyone who's looking for a new data engineer?" I haven't been able to help anyone find a new job yet, but if this keeps happening, I might. So, if you're looking, let me know. PS. I did help the second company - we went through the problem space and figured out a better architecture. It also didn't require hiring.