Emmy Owens’ Post

freshers and experienced ;)

Data Engineer vs Data Analyst vs Data Scientist: Career Guide for 2024 https://lnkd.in/gKXpvGSM This article provides a career guide for data science jobs like data engineer, data analyst, and data scientist in 2024. The job roles of Data Engineer, Data Analyst, and Data Scientist differ but are interconnected. #DataEngineervsDataAnalystvsDataScientist #DataEngineer #DataAnalyst #DataScientist #DataScienceJobsin2024 #AINews #AnalyticsInsight #AnalyticsInsightMagazine

Hiring Alert ❗❗❗ LinkedIn & Societe Generale are hiring for the below mentioned roles 🌎 🍃 Big Data Developer 🍃 Senior Data Engineer links available in the comment section to apply directly 💻 Hit the like button 👍🏼 or comment 'interested' to help your connections ♥️ follow Asheesh .. for more updates 📲 #dataengineer #engineer #bigdata

Many data analysts are leaving their positions to become data/analytics engineers. Having consumed (bad) data for years, they know better than anyone the importance of: - high quality data - smoothly running pipelines - robust data models - extensive documentation So to all companies in desperate search of data engineers out there: you might want to look into hiring some hungry DAs. Their past experience might provide the exact fit you're looking for 🤓 #dataanalytics #dataengineering #trend

Insightful read on the evolving landscape in data careers! The shift from data analyst to data/analytics engineer reflects a deeper understanding of the critical aspects of data management. Data analysts primarily focus on interpreting and gaining insights from data, while data engineers concentrate on designing and constructing systems that collect, manage, and convert raw data into usable information. This transition underlines the importance of a strong foundation in both the interpretation and the technical handling of data. It’s a testament to the value of hands-on experience with data, highlighting the importance of quality, efficiency, and robust systems. This perspective might just be the key to unlocking new potential in the field of data engineering. #DataAnalytics #CareerTransition #DataEngineering #IndustryInsights

No wonder there are so many AI PMs these days 😂 😅 Some ideas for those feeling a bit of imposter syndrome ✨ 🤖 Though AI is peak hypecycle, it is inspiring to see so many folks upskilling, investing in themselves, and sharing their learning to help the industry understand what is possible in mature data environments. We know what comes next and that's where the work becomes easier lives for humankind. 🙏 Feeling truly privileged to be part of such a #growthminded community willing to help one another and on a journey to transform life to work for us. ⏳ We just have to have the patience to develop the data pipelines, build the teams, and discover the problems worth solving. There's still so much groundwork to lay. For those feeling a bit behind, there are so many wonderful mentors in the data product space. 🧠 Want to upskill? - Pendo has a great 2-hour AI for PMs course (for free!) - DataCamp has AI Fundamentals & Data tracks (50% off thru 1/25!) - Udacity, Coursera have countless courses - Prompt Engineering from OpenAI basics - Meetups, conferences, coffee chats are some of our best resources - Don't forget investor resources and startup coverage 💹 What are your favorite resources? #AI #productmanagement #hypecycle #jobopenings #data #machinelearning #llms #chatgpt

There is not a subclassification of data engineer so it looks like there is a huge demand for data engineering. Are the prerequisites for AI Product Manager, Machine Learning Engineer, MLOps Engineer (ML part), Data Analyst, and Data Scientist similar? In addition, initial effort for getting a DS&ML (or similar) roles is much more than data engineering. Algorithms were built on top of maths(statistics, linear algebra, etc) and new algorithms are not discovered so frequently. When you learn them, learning curve may be smoother. #dataengineering #datascience #machinelearning On the other hand, initial effort for data engineering is less that former. However, in each year new cloud services or open source tools are being created. Tech stack and market demands are changing so frequently. And companies prefer different technologies. In company A, a data engineer is using Spark. When he/she started to work at company B, he/she needs to learn Flink, Kafka or GCP. For that reason, you need to learn and adapt continuously.

I always thought Data Analyst are the most in demand positions in Data. It turns out I was wrong. Here is an actual number of openings on LinkedIn per each Data related job as of January 2024. Here is a list in DESC order 👇 🔎 𝐀𝐈 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 - 39,610 openings 🔎 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 - 37,517 openings 🔎 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 - 30,836 openings 🔎 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 - 23,954 openings 🔎 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 - 9,901 openings 🔎 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 - 6,633 openings 🔎 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 - 6,582 openings 🔎 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 - 4,561 openings 🔎 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 - 3,441 openings Did you think the same? #dataanalytics #datascience #jobhunt

#hiring Data Analyst II, San Diego, United States, fulltime #jobs #jobseekers #careers #SanDiegojobs #Californiajobs #ITCommunications Apply: https://lnkd.in/dtQAU8qZ Are you ready to be a part of an organization that strives to connect people to resources, and partner with our community to transform how people access help? Then read on: We are looking for a detail-oriented Data Analyst II. You will be responsible for building and sustaining an analytic portfolio with a focus on external-facing products. The Data Analyst II strategically supports the Informatics team as well as internal business units to analyze key data and encourage data-driven decisions. The Data Analyst II must be able to use data to tell a compelling story, who possesses both strong technical skills as well as a passion for using data to positively impact change in our community. Residents of San Diego County need only apply The first 6-month of work is in office, with opportunity to work hybrid work schedule thereafterSign-on bonus eligible Make a Difference through ActionBuild a robust and sustainable analytic portfolio with a focus on external publications, analyses, and products. Create and own an analytic roadmap, working with key stakeholders to outline long-term vision, and clearly communicate timelines and deliverables to internal leadership. Perform descriptive and predictive analyses to evaluate key strategic questions and provide specific insights to support internal operations; create presentations to synthesize complex analyses into actionable summaries and tangible recommendations.Develop infrastructure to bring in key external datasets into analyses. Monitor external data sources to compile and format vital trend information for community planning.Collaborate with technology teams to integrate insights into 2-1-1 San Diego Client Management System.Collaborate with Client Services teams to integrate insights into program planning.Develop advanced SQL queries and tables within the Informatics Databases to allow for consistent and standardized data analysis with a focus on defining and standardizing client outcomes; develop novel and creative data metrics leveraging an expansive data warehouse.Create compelling and reader-friendly infographics, tables, graphs, maps and other presentation visuals to communicate complex ideas, issues and trends gleaned from statistical reports.Design and implement Tableau dashboards to visualize analyses, monitor trends, and provide real-time data to stakeholders.Ensure validity of critical data is accurately collected. Troubleshoot data issues and work in collaboration with internal staff to identify and correct issues and ensure validity and efficacy of data.Project manage external data requests by providing administrative support; track data, research, and evaluation requests by processing applications, obtaining executed legal agreements, and provisioning datasets.Re