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‼️ Amazon is Hiring for Data Engineers ‼️ 🙋🏻 Link to apply : https://lnkd.in/g9xtKgbk 📚👨🏻🎓 ** Learn these to become an outstanding Data Engineer ** 👷🏼♂️: 1. Actual spark architecture understanding ( not just Databricks, but what happens under the hood ) 2. DE with software engineering : DE is a lot like Software development. Testing, CI/CD etc must be a norm not an option. 3. Willingness to learn : it's important to highlight this quality. We look for people who are happy to discuss a problem even if they don't completely understand the tech behind it. 4. ⁠Fundamentals of data engineering: things like data validation, consistency, creating I idempotent pipelines. ** Check out my topmate profile for Data Engineering learning resources ** #hiring #data #dataEngineer

One of the advantages of being a data enthusiast is the ability to transfer skills into most high paying careers. Especially the new wave of A.i careers. Some of these careers where a Data expert can thrive in include: 1. Data Scientist: Transferable Skills: - Data wrangling and pre-processing expertise - Exploratory data analysis (EDA) and insights generation - Statistical proficiency and experiment design - Effective communication of complex findings 2. Deep Learning Engineer: Transferable Skills: - Programming proficiency, particularly Python - Strong foundation in mathematics and statistics - Problem-solving and analytical approach - Data preparation and feature engineering capabilities. 3. Machine Learning Engineer: Transferable Skills: - Familiarity with common machine learning algorithms - Experimental design and model evaluation skills - Model deployment and monitoring awareness - Clear communication of technical concepts to diverse audiences. It’s 2024, Date roles are the future !!

Data scientists: Considering becoming a software engineer? We're breaking down the pipeline on where these two roles overlap and what you need to know to transition from data science to software engineering: https://lnkd.in/gCviZkUc #datascience #softwareengineering #careerchange

In the dynamic realm of data, Heads of Data play a crucial role in assembling robust teams that drive innovation forward. When it comes to hiring your next Data Engineer, keep these three pivotal tips in mind: 1. **Technical Expertise**: Beyond proficiency with programming languages such as Python or Java, look for in-depth experience with database management systems, ETL tools, and cloud platforms like AWS or Azure. Understanding of data modeling and warehousing is also indispensable. 2. **Problem-Solving Skills**: Data engineering isn't just about handling datasets—it's about solving complex problems. Candidates should demonstrate strong analytical thinking, with a track record of developing creative solutions to challenging data problems. 3. **Collaboration and Communication**: Effective data engineers often work at the intersection of multiple teams and stakeholders. They must be adept in articulating technical concepts to non-technical team members and collaborating across departments to understand and meet the company's data needs. Invest in those who can master the complexities of data while driving your organization towards a data-centric future. #DataEngineering #RecruitingInsights #TechTalent

#Hiring - Mid/Senior Software Engineers (Data Pipeline) - Scientific Data Cloud Series B company that is a pioneer in their sector, serving as the crucial link between the extensive data repositories of BioPharma and Life Sciences enterprises, and the training of large language models. This team is empowering scientists by giving them better tools to manage their data, ultimately accelerating scientific breakthroughs and improving human health and longevity. Comment or DM if you or someone you know might be interested. Disclosure: Currently only able to consider US Citizens and GC Holders - cannot support H1 transfers #Python #TypeScript #NodeJS #ApacheSpark #AWSglue #datainfra #datapipeline #bioinformatics #datacloud #scientificdata

good opportunity

‼️ Amazon is hiring for Data Engineer ‼️ Link to apply : https://lnkd.in/gPfs768v Junior Data Engineer Expectations : 1. Mid level at Data Structures & Algorithms. 📊 2. In data structures and Algorithms: Good at string, array, data retrieval - like problems (Binary search) 🧩 3. Knowledge of advanced SQL 📚 4. Can write code with Spark ⚡ 5. Experience with airflow/cron jobs 🔄⏰ 6. Python or scala understanding 🐍🔄 7. Basic experience with cloud services ☁️ 8. Data engineering system design (Batch) 🛠️ Nice to have: 1. Data engineering system design (Streaming) 🌊 2. Basic knowledge of Kafka 🔄📊 3. Data warehousing knowledge 🏢 4. NoSQL Databases 📄 5. Writing test cases ✅ Follow Nitesh Chaudhry for more on Data ! #data #dataEngineering #hiring

Data Engineering ≠ Data Science ≠ Software Engineering 👨💻👩🔬 Many companies make the mistake of misaligning roles. 👨🏫 Data engineers writing REST APIs, data scientists building pipelines, and software engineers building models. Hire specialists for their specific skills. Don't force them into inefficient generalist roles! Hit the 🔔 on my profile John K. Moran and share your thoughts in the comments.👇 #dataengineering #softwareengineering #datascience

AppSierra is hiring through Pitch N Hire N Hire for its client, a skilled ML Engineer. Location: Remote Exp: 3-7 years Apply Here: https://lnkd.in/gqCAbuyD #machinelearning #artificialintelligence #ai #datascience #python #technology #programming #deeplearning #coding #bigdata #computerscience #tech #data #software #dataanalytics #pythonprogramming #developer #datascientist #javascript #programmer #java #innovation #ml #coder #robotics #webdevelopment #analytics #html #linux

Hi, Hope you are doing great!   Please find the below mention JD.   Job Title: Machine Learning Engineer Lead Fully Remote: Yes, EST Duration : Contract Need USC GC Technical Skills and Expertise Machine Learning Proficiency: Strong understanding of machine learning algorithms and principles, especially those relevant to anomaly detection, such as supervised and unsupervised learning, clustering, and neural networks. Data Handling: Expertise in managing, processing, and analyzing large datasets. Familiarity with data cleaning, data transformation, and feature engineering is crucial. Programming Skills: Proficiency in programming languages commonly used in machine learning such as Python. Model Development and Evaluation: Ability to develop robust ML models and evaluate their performance through metrics specific to anomaly detection. System Integration: Skills to integrate ML models into existing systems for seamless data flow and model deployment including Azure big data and analytics experience. #machinelearning #mlengineer #gc #usc #contractjobs #c2cjobs #benchsales