Kirstie Moffat’s Post

Question for all the more senior Data Engineers in my network What’s next? I recruit for tons of Data Engineer roles and I’ve seen the candidates I’ve worked with over the years go on to have incredible careers. But the career paths can be so varied. Some have gone on to be people managers as Head of Data Engineering. I've seen a Data Analyst go on to become a Lead Data Architect! Others have opted to become contractors and get paid massive sums. Some have moved into strategic and architectural roles. But for a lot of more junior Data Engineers, they may not know exactly what to do next. So, for senior data engineers and leaders in my network, what advice would you give to your younger self when deciding which path was right for you? Comment below, you may be helping someone in a big way! #dataengineering #recruitment #datapipelines #careers #growth

🤷♂️ What is a job role of a Data Scientist? 🚀 ✅ An analytics specialist known as a "data scientist" is in charge of gathering, examining, and interpreting data to support organizational decision-making. A mathematician, scientist, statistician, and computer programmer are just a few of the conventional and technological occupations that are combined in the data scientist function. It incorporates the application of scientific concepts with the use of sophisticated analytics methods like machine learning and predictive modeling. ✅ In order to develop and test hypotheses, draw conclusions, and analyze things like customer and market trends, financial risks, cybersecurity threats, stock trades, equipment maintenance needs, and medical conditions, data scientists frequently have to work with large amounts of data as part of data science initiatives. ✅Data scientists work with organizations to find information in data that may be utilized to anticipate consumer behavior, spot fraudulent transactions, find new income streams, and fulfill other business requirements. For healthcare providers, educational institutions, governmental organizations, sports teams, and other kinds of organizations, they also provide important analytical work. Excited!! If you're interested in pursuing a career as a data scientist, contact us by email at 'connect.dombivli@bostoninstituteofanalytics.org' or by calling 9004063651.

🚀 SQL Time 🚀 Imagine you're managing an Employee database for a tech company. Your task is to generate a report on high-salary employees with specific job titles. The goal is to order the employees first by their roles and then by their salary within each department. You need to retrieve employee details, including their IDs, job titles (specifically 'Software Engineer' and 'Data Scientist'), hire date, and salaries from the Payment table. #sql #database

Aleut Federal is #hiring a Data Scientist III This Hybrid opportunity is located in the Washington, DC office. 🎇 👀Data Analysis & Interpretation: Analyze large, complex datasets to uncover trends, patterns, and insights that inform strategic decisions. 👉Model Development: Develop and implement machine learning models, algorithms, and statistical techniques to solve complex problems. 👉Data Management: Clean, preprocess, and organize data from diverse sources ensuring data integrity and accuracy. 👉Collaboration: Work collaboratively with other data scientists, analysts, engineers, and stakeholders to understand requirements and deliver impactful solutions. 👉Visualization: Create clear, concise, and compelling data visualizations and reports to communicate findings to non-technical stakeholders. 👉Research & Innovation: Stay abreast of the latest advancements in data science, machine learning, and artificial intelligence to continually improve methodologies and processes. 👉Security Compliance: Ensure all work complies with security protocols and maintains the confidentiality of sensitive information. Apply Here ⬇ https://lnkd.in/ep6SqWrV John L. Papp, Jr. & Allison Mengel

🔴Dark Side of Data Engineering Field and My Failures❌ In the initial phase of career i was so much confused🤯 ✅Which Tool to Learn ✅Which Field to Focus upon ✅What all Technologies should i add in resume ✅What personal projects should i add And certainly i failed numerous of times, while trail and testing the things😬 But it has been 3.3 Years now in Data Engineering Field and i can say that things are now kn track🧿 So, if you’re an aspiring Data Engineer or Data Analyst or Data Scientist this phase will definitely come in your career. So i tried to sum up all those experiences and learnings in one video so that you can save your time 🕰️ 📍Link for the video -: https://lnkd.in/gnCHTmhn #databrickscertified #dataengineering #dataengineer

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

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

3 things Data Scientists NEED on their CV in 2024 (Number 3 is obvious, but often missed out 😬) Yes, 2024. We're getting ahead of the game here at Hainton. 📄1. Information SPECIFIC to the role you are applying for. The temptation is to show off everything you've ever done but remember, the person reading your CV might not have the technical knowledge to understand it 👀 If the job description requires experience in clustering algorithms and you list K-Means, the person hiring might not know that they're the same thing and assume you don't fit the criteria. Your CV gets chucked in the discard pile all due to miscommunication 🗑️ ⭐️Tip: Make sure you're including evidence of your experience that directly aligns with the job description (word for word) 📄2. Don't assume they know what you're doing now Just because you have a skill listed in a previous role, doesn't mean that the recruiter will assume you're still using it now. Many recruiters, when looking for data scientists, will be hunting for recent relevant experience. ⭐️Tip: Even if you think it's obvious that you're still XYZ, list it! 📄3. Data, data data Wherever you can, back up the points on your CV with real, solid data. Demonstrate the achievements you made, and how this benefitted your team and the company - especially time and/or money you saved. ⭐️Tip: Show, don't tell! Remember, at this stage, it's not necessarily the best candidate who gets chosen, but the best CV. Hit all 3 points above and you're in with a better chance of getting to the interview stage where you can prove your credibility. 🔎TLDR; Don't assume that the recruiter knows everything about data scientists. Use laymans terms and, wherever possible, hit the key points in the job description. On the hunt for your next Data Science role? You can book a call with me here: https://lnkd.in/d5aWQkv3

It doesn’t matter if you're a Data Engineer, Data Scientist, or Data Analyst. It doesn’t matter what title you hold. You’ll still need to: 1. Handle missing or inconsistent data. 2. Optimize SQL queries for better performance. 3. Communicate findings to non-technical stakeholders. 4. Integrate data from multiple sources. 5. Ensure data security and compliance are met. 6. Titles may change, but the core responsibilities remain the same. Do you agree?

For people currently job-hunting on the #datascience or #dataengineering market or others who have already gotten hired, what is your experience with take-home technical assignments? How much is too much? What types of questions do you think are the best way to show what you’re capable of as a data scientist or engineer? What format is ideal? What hasn’t worked? I do feel the field has historically never gotten this part right and curious to hear thoughts on the best way to do technical interview with future candidates.