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🔔 #nowhiring - Machine Learning Architect (m/f/d) | #Lisbon (Hybrid) Want to enhance their data science area? If you have an amazing knowledge in data science solution lifecycles let's have a conversation! 🌐 Link: https://zurl.co/qvgF #AI #azure #mlops #datascience

AI may automate some tasks in data engineering, but it's unlikely to replace data engineers entirely. Data engineers will continue to be vital for designing, building, and maintaining data infrastructure, ensuring data quality, and addressing complex data challenges that require human expertise and oversight. what do you think? can AI facilitate improvement for data engineers or is it considered as a career threat to them? #VTRAC #job #jobopportunity #Canada #data #dataengineering #manager #director #databricks #datawarehouse #datalake #snowflake #Azure

What is Data Engineering, and Why is it Important? In today’s data-driven world, Data Engineering plays a crucial role in turning raw data into actionable insights. As data continues to grow exponentially, businesses need reliable and scalable pipelines to process, store, and manage their data. So, what does a Data Engineer do? Build Data Pipelines: We create automated pipelines that move data from various sources to storage solutions, transforming and cleaning it along the way. Optimize Databases: We ensure that data is stored efficiently and is easily accessible for analysis. Collaborate with Data Teams: Working alongside data scientists and analysts, we ensure they have the high-quality data they need to drive decision-making. Leverage Cloud Technologies: With cloud platforms like AWS, Azure, and GCP, we build scalable, cost-effective data solutions. Why is Data Engineering essential? Without clean, structured data, it’s impossible to get meaningful insights. Data Engineers bridge the gap between raw data and business intelligence, empowering organizations to make informed decisions. Interested in learning more? Let’s connect and discuss how Data Engineering shapes today’s business landscape! #DataEngineering #DataPipelines #Cloud #BigData #ETL #Analytics

Demand for skilled Data Analytics professionals has reached unprecedented heights. Our latest blog delves deep into the future of Data Analytics careers, exploring emerging trends and exciting opportunities that lie ahead. From AI and machine learning integration, to the democratisation of big data through cloud computing, the field of Data Analytics is undergoing a seismic shift. As every industry increasingly relies on data-driven decision-making, Data Analytics professionals are poised to become invaluable assets, driving innovation and shaping the future of business. Check out our latest blog uncovering key insights and strategies to thrive in the ever-evolving world of Data Analytics careers. #DataAnalytics #CareerInsights #AnalyticsJobs #Career #futureofwork #recruitment #hiring https://lnkd.in/gSbnbZhx

🔧 The Vocabulary of an Azure Data Engineer 🔧 Ever wonder what the day-to-day life of an Azure Data Engineer sounds like? It’s a mix of technical jargon, cloud concepts, and a whole lot of problem-solving. Here are some of the most common words that fill our conversations and drive our workflows: Pipeline: The lifeblood of our work, connecting data from diverse sources, transforming it, and delivering it where it’s needed most. Data Lake: Where we store vast amounts of raw, structured, and unstructured data, waiting to be processed and analyzed. ETL/ELT: Extract, Transform, Load—or sometimes Extract, Load, Transform. Either way, it’s how we move and shape data to make it useful. Integration: Bringing together data from multiple sources, ensuring it all works harmoniously within the Azure ecosystem. Orchestration: Coordinating complex data workflows, making sure every process runs smoothly, on time, and in the right sequence. Monitoring: Keeping a close eye on performance, ensuring that pipelines run efficiently and data flows seamlessly. Scalability: Ensuring that our solutions can grow with the increasing data volumes, without missing a beat. Governance: Protecting the quality, integrity, and security of our data, making sure it complies with all necessary regulations. Synapse: Our go-to for big data analytics, allowing us to query and analyze data at scale with unmatched speed. Automation: Streamlining repetitive tasks so we can focus on solving bigger challenges and innovating faster. These words are more than just buzzwords—they’re the building blocks of our daily work. They represent the challenges we tackle, the solutions we create, and the impact we have on our organizations. If you’re an Azure Data Engineer, you probably know these terms by heart. And if you’re just starting out, get ready to make them a key part of your vocabulary! Let’s keep the data flowing and the innovation growing! 🚀 #AzureDataEngineer #DataEngineering #CloudComputing #DataPipelines #BigData #Azure #TechTalk #Innovation

INCORPORAN INC is looking for Machine Learning - AI Architect for our client in the location is Remote   Please send me your resumes and contact details at [email protected] or call Rajesh at 609-474-4722   Job Description:   Required skills: AWS, Python, Airflow, Kedro, or Luigi Hadoop, Spark, or similar frameworks. Experience with graph databases a plus. 1.           Designing Cloud Architecture: As an AWS Cloud Architect, you’ll be responsible for designing cloud architectures, preferably on AWS, Azure, or multi-cloud environments. Your architecture design should enable seamless scalability, flexibility, and efficient resource utilization for MLOps implementations.   2.           Data Pipeline Design: Develop data taxonomy and data pipeline designs to ensure efficient data management, processing, and utilization across the AI/ML platform. These pipelines are critical for ingesting, transforming, and serving data to machine learning models.   3.           MLOps Implementation: Collaborate with data scientists, engineers, and DevOps teams to implement MLOps best practices. This involves setting up continuous integration and continuous deployment (CI/CD) pipelines for model training, deployment, and monitoring.   4.           Infrastructure as Code (IaC): Use tools like AWS CloudFormation or Terraform to define and provision infrastructure resources. Infrastructure as Code allows you to manage your cloud resources programmatically, ensuring consistency and reproducibility.   5.           Security and Compliance: Ensure that the MLOps architecture adheres to security best practices and compliance requirements. Implement access controls, encryption, and monitoring to protect sensitive data and models.   6.           Performance Optimization: Optimize cloud resources for cost-effectiveness and performance. Consider factors like auto-scaling, load balancing, and efficient use of compute resources.   7.           Monitoring and Troubleshooting: Set up monitoring and alerting for the MLOps infrastructure. Be prepared to troubleshoot issues related to infrastructure, data pipelines, and model deployments.   8.           Collaboration and Communication: Work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders. Effective communication is essential to align technical decisions with business goals. 

Glad to meet you all with an new article on AI architect!! AI architects work in the field of information technology to develop and implement infrastructure for applications, databases, and computer networks. When it comes to governing and scaling AI efforts, they serve as the connecting tissue between data analysts, database administrators, programmers, operators (DevOps, DataOps, MLOps), and business unit executives. AI Architect Job Role: 1. Due to the fact that AI has a wide variety of deployment patterns and use cases, AI architects need to be capable of performing the following duties: 2.Assist digital transformation initiatives with the help of data scientists and AI experts by finding and testing use cases. Consult with business stakeholders on the viability of use cases and architectural style to help transform the objective of business executives into a technological execution that can be achieved. Also, draw people's attention to efforts that aren't complementary or use cases that won't work. 3.Gather feedback from a wide variety of parties, including corporate customers, data analysts, security specialists, data engineers and strategists, and the IT operations department, and use that information to shape the procedures and final products so that they are in line with current and future needs. Take the lead in deciding the open-source and commercial tools to use to build the AI and design its architecture. Choose a deployment type (cloud, on-premises, or hybrid) and make sure the new tools work well with the ones that are already there for data management and analytics. #snsinstitutions #snsdesignthinkers #designthinking

As EMEA Sales Director @ GetDataInsight, I like to share that we blend technology with data for effortless real-time intelligent automation.  We set out to change the Intelligent Automation market for the better. To develop the right “fit-to-purpose”-built stacks with user experience, accessibility and security in mind. To provide a true intelligent data, AI and orchestration engine that empowers any intelligent automation scenario and ultimately enable our customers with infrastructure and centralized control needed to better manage and scale.  We are full stack engineers, cloud architects, DataOps, data scientists, and we’re all passionate about working together towards digital transformation. We take an end-to-end, innovation-led approach to helping our customers “imagine and invent” their futures. SUMMARY We can offer companies the below mentioned products, services and benefits: - Ready-to-go DataOps, AIOps & MLOps platform - "Fit-to-purpose" built accelerators and building-blocks - You don't have to migrate off your current tech stack with our accelerators - You can keep your existing tech stack by putting components together as they see fit - Run our platform your way GETTING STARTED @ +31615891133 & [email protected] #data #intelligent #automation #ai #artificialintelligence #artificialintelligenceforbusiness #technology #realtime #realtimedata #stack #stacks #security #orchestration #fullstackengineer #fullstackdeveloper #fullstack #cloud #cloudarchitect #cloudarchitecture #dataops #dataoptimization #datascientist #datascientists

There has been an ever-increasing demand for Azure Data Engineers recently due to exponential growth of cloud-based data analytics, AI, and machine learning solutions. Organizations are seeking skilled professionals to manage and optimize large-scale data pipelines, enabling smarter decision-making and innovation. Do you wish to be one of them? Here is an interview scenario for the questions on storage account service and possible responses from the candidate: Interviewer- What are the key differences between Blob and ADLS? Candidate- Blob and ADLS differ in their nature based on following: 📁 Data Organization ⚙ Analytics and Processing 💡 Use Case 🔐 Security 💰 Cost At foundation, blob provides a flat storage structure which is primarily used for unstructured data whereas ADLS supports hierarchical namespace that allows data to be organized in hierarchical structure which is perfect for data analytics and processing. Interviewer- Can you tell me a use case of Blob and ADLS? Candidate- Blob being a simple storage service can be a good use case for social media platforms that deal with large volumes of media like images, videos, audios etc. ADLS on the other hand can be leveraged by organizations for the use case of running the large volume of transaction data like sales, customer, operations etc. for performing various types of analysis. Interviewer- Can you elaborate on Security and Cost aspects as well? Candidate- Blob provides basic security features, including SAS (Shared Access Signatures) and RBAC (Role- Based Access Control). Encryption is provided both in transit and at rest. Blob is generally cost-effective for storing large amounts of unstructured data that is not frequently accessed. In addition to SAS tokens and RBAC, ADLS offers file and folder-level security using ACLs (Access Control Lists), which gives more granular control over data access. ADLS is typically more expensive due to its enhanced features (hierarchical namespace, ACLs, etc.) and the specialized nature of big data workloads. Watch this space for more such scenarios. #azureinterviewquestions #azuredataengineer #learnazure #dataengineering #hotskill

As businesses continue to lean on data-driven decision-making, the role of a Data Engineer is more critical than ever. Our work not only ensures data flows smoothly but also enables impactful insights that drive innovation and growth. 💡 💻 Here's how Data Engineering is making waves: Building scalable data pipelines to handle massive datasets efficiently. Integrating cloud platforms like AWS, Azure, and GCP to optimize performance. Ensuring data quality & security to protect sensitive information. Leveraging tools like Spark, dbt, and Snowflake to transform raw data into actionable insights. Over the next 30 days, I’m sharing tips, best practices, and case studies on how Data Engineers can supercharge their workflows and make a tangible business impact! If you're in the field or aspiring to be, stay tuned for practical knowledge and exciting discussions. 🔥 Feel free to connect with me or drop a message if you're passionate about data, innovation, or collaboration in this evolving field! Let’s build a thriving Data Engineering community together. 🙌 #DataEngineering #BigData #CloudComputing #MachineLearning #ETL #DataPipelines #AI #DataDriven