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Unlocking the Talent Puzzle: Navigating the Challenges of Hiring Data Architects in Today's Competitive Landscape Hiring a Data Architect is an exceedingly difficult task only because the ideal candidate for a role both has to have all those technical skills and knowledge you need in order to enforce but also be someone with vision and leadership, who will drive innovation & transformation throughout the organisation. However, in the brief summary of data architecture that was just provided above, we touched on some lagging trends around databases and algorithms. A high-quality data architect has to stand aside and be able to fit the future, forecast trends for upcoming years & design scalable so performant solutions aligning with the strategic goals of their organization. On top of that, elite data architects are frequently in high demand and offer is scarce itself - making it a tight market for businesses to recruit from. With remote work, it puts the responsibility on organisations to actively recruit by approaching their networks and offering competitive compensation in order to entice highly sought-after candidates. In addition, a data architect role has to be strong in communication and collaboration as they will work with cross-functional teams that include stakeholders to C-level executives helping convert business requirements into technical solutions. In this role, you need to find someone who can speak the language of both business and technology. In the end, hiring a data architect is all about finding one who possesses not just technical skills but also has strategic vision and leadership to create significant impact in your organisation by leveraging information as an asset.

Principal Enterprise Data Engineer by via Remote jobs from Himalayas ([Global] Oracle Advanced Analytics) URL: https://ift.tt/05oIDq6 Job TypeFull-timeDescription Come to work each day with an inclusive and collaborative Data Team! As a Principal Enterprise Data Engineer in Stax, you’ll have opportunities to contribute to the digital transformation of a leading Fintech company, helping to create solutions that enhance our client’s ability to deliver a best-in-class payment experience for their customers. Stax is seeking an individual to champion our team’s ability to design, development, and implement data solutions aimed at empowering the organization to derive actionable insights from intricate datasets. You will take the lead in guiding, as well as working with, a team of Data Engineers, Data Scientists, and BI Analysts. You will also work with the IT and Business Teams to foster collaboration amongst the cross-functional teams, and spearhead initiatives geared towards fortifying our data infrastructure, CI/CD pipelines, and analytics capabilities. Responsibilities: Act as a technical resource for The Data Team; sought out for particularly thorny problems. Advises The Data Team teammates on improving selection of methods and techniques. Proactively drive change in how business and technical users approach access, data quality, data movement, and data storage problems, improving their analysis, development, and access. Can identify unmet data architecture needs and work with the Data Team to fill them, acting as the project manager from ideation to completion. Proactively looks for ways to improve data architecture team tools and methods, reports back to team. Utilize up-to-date coding, testing, security, DevOps, and software engineering practices to create and maintain data solutions. Utilize programming/scripting languages to enhance automation and REST/Graph APIs to create, maintain, and automate reporting solutions. Engage in migration opportunities from current supported technologies to cloud based solutions such as AWS cloud hosted solutions. Create documentation and standards for what "best practices in designing and developing data architecture" means. Can develop an executive-ready data architecture project, report or presentation with little instruction. Develops data architecture in support of KPIs reporting and analysis for their area of the business Lead planning, building and running enterprise class information management solutions across a variety of technologies (e.g. big data, master data, data profiling, batch processing, data modeling, and data indexing technologies,). Ensure appropriate policies are followed for information access, analysis, and dissemination. Responsible for the analysis, design, deployment, maturity, and support of data solutions. Define and apply information quality and consistency business rules throughout the data processing lifecycle. Collaborate with information provide...

#hiring Sr. Director, Data Science, Philadelphia, United States, fulltime #jobs #jobseekers #careers #Philadelphiajobs #Pennsylvaniajobs #ScienceTechnology Apply: https://lnkd.in/gsakkzJN Comcast brings together the best in media and technology. We drive innovation to create the world's best entertainment and online experiences. As a Fortune 50 leader, we set the pace in a variety of innovative and fascinating businesses and create career opportunities across a wide range of locations and disciplines. We are at the forefront of change and move at an amazing pace, thanks to our remarkable people, who bring cutting-edge products and services to life for millions of customers every day. If you share in our passion for teamwork, our vision to revolutionize industries and our goal to lead the future in media and technology, we want you to fast-forward your career at Comcast.Job SummaryResponsible for extracting knowledge and insights from high volume, high dimensional data in order to investigate complex business problems through a range of data preparation, modeling, analysis and/or visualization techniques, which may include the use of advanced statistical analysis, algorithms, predictive modeling, experimentation and pattern recognition to create solutions that enable enhanced business performance. Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights and solutions for client services and product enhancement. The team is composed of experts in artificial intelligence, deep learning, data-structures, algorithms, distributed systems, and system performance and analysis. The systems that the team builds get used across the multitude of Company data science-based services and deployments. Provides leadership and direction for diverse and complex functions. Contributes to the development of the organization's business strategy. Interprets business strategy and develops organizational objectives to align with this strategy. Typically manages multiple teams of professionals.Job DescriptionCore ResponsibilitiesInteracts with product and service teams to identify questions and issues for data analysis and experiments.Leads development and coding of software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources.Manages teams that use analytical rigor and statistical methods to analyze large amounts of data, extracting actionable insights using advanced statistical techniques such as data analysis, data mining, optimization tools and machine learning techniques and statistics

Data Architect Evaulation So you are thinking about hiring a data architect. You want a person to oversee your data and lead you to the next level of competency. You will need a clear vision of how this person will fit within your organization and your expectations for the tasks they will responsible for. Data is threaded throughout the fabric of your company. It touches every layer from HR to applications to analytics. A great architect will be a disruptor. They will want to change and improve. The best ones will co-opt each and every team to be part of the solution. The worst will either throw a hand grenade into the pond, or avoid any kind of conflict (advanced turtle). A great architect will want action not just recommendations, but planned action. The data items hidden in the technical debt will percolate back to the surface. A great architect will expect transparency. They will start with architecture, move on to roadmaps and then review your changes/defects. It may seem annoying, but it is the fastest way to identify the strength and stability of your data. This is the team that the app teams need to reposition themselves as part of the solution and not part of the problem. A great architect will inspire excellence. You will do things better with more robust systems and better practices. Your staff will grow and learn. I think it is obvious now that you need to think seriously about how they will fit, the scope you will allow them. You need to be upfront about this and not hedge around it. You also need to think carefully about the interview process. It is not about asking SQL questions and database questions, it is the strategy that matters most. #data

Data Analyst or Analytics Engineer or Data Engineer, are you confused about these terms, and don't know where to start, what skill sets each role require and how these roles differ from each other in a modern data team. Dive into this wonderful article to understand the difference b/w these roles, traditional vs modern data team and a refresher on what is analytics engineering. https://lnkd.in/gEc5grYc #analyticsengineering #businessintelligence #analytics #dbt

POST №2. MAIN POSITIONS (ROLES, VACANCIES) IN THE FIELD OF ANALYTICS. (Part 2) Category №2.  Date engineering. Data Modeler is a specialist who ensures the correct organization of a business' data and its optimization in an information system within the framework of certain business processes. In fact, it is a mixture of Systems and Business Analysts who work with data architects and database administrators on the design of computer databases, a specialist without whom suitable computer systems are not created. So, they evaluate, organizes, and measures data within certain business processes. Data Engineer is a person who creates and maintains data processing pipelines, collects data from various sources (structured and unstructured), cleans and transforms data and makes it available to data processing specialists and analysts. The difference from a Data Warehouse Developer: A broader range of responsibilities (beyond the data warehouse), including managing data pipelines, processing large amounts of data and working with various data sources and technologies. Data Platform Engineer (a more advanced version of a Database/Data Lake Administrator) is a specialist responsible for configuring and managing data platforms, such as data warehouses, data lakes and data processing systems. It also manages the performance, scalability, and security of the system. A Data Platform Engineer can work to organize data, monitor, and optimize the data infrastructure. The difference from a Data Engineer: Pays attention to infrastructure and system management, while a Data Engineer concentrates on data manipulation and ETL processes. Category №3.  Data Scienceing. Machine Learning Engineer is a person responsible for the design, development and deployment of machine learning models and systems. They are working on creating algorithms that allow machines to learn from data and make predictions or decisions. In fact, they work with data, then only for training, improving, and deploying machine learning models. Data Scientist - a role that covers data analysis, hypothesis formulation, experimental design, and the use of data to confirm or refute these hypotheses, statistical modelling, machine learning. This specialist can be formulated as follows: A combination of a Machine Learning Engineer and a Data Analyst less focused on everyday, tactical decision-making. #dataanalysis #businessintelligence #data #selfdevelopment #value #business #dashboard #information #knowledge #career #teamdevelopment

Principal Enterprise Data Engineer by via Remote jobs from Himalayas ([Global] oracle cloud) URL: https://ift.tt/05oIDq6 Job TypeFull-timeDescription Come to work each day with an inclusive and collaborative Data Team! As a Principal Enterprise Data Engineer in Stax, you’ll have opportunities to contribute to the digital transformation of a leading Fintech company, helping to create solutions that enhance our client’s ability to deliver a best-in-class payment experience for their customers. Stax is seeking an individual to champion our team’s ability to design, development, and implement data solutions aimed at empowering the organization to derive actionable insights from intricate datasets. You will take the lead in guiding, as well as working with, a team of Data Engineers, Data Scientists, and BI Analysts. You will also work with the IT and Business Teams to foster collaboration amongst the cross-functional teams, and spearhead initiatives geared towards fortifying our data infrastructure, CI/CD pipelines, and analytics capabilities. Responsibilities: Act as a technical resource for The Data Team; sought out for particularly thorny problems. Advises The Data Team teammates on improving selection of methods and techniques. Proactively drive change in how business and technical users approach access, data quality, data movement, and data storage problems, improving their analysis, development, and access. Can identify unmet data architecture needs and work with the Data Team to fill them, acting as the project manager from ideation to completion. Proactively looks for ways to improve data architecture team tools and methods, reports back to team. Utilize up-to-date coding, testing, security, DevOps, and software engineering practices to create and maintain data solutions. Utilize programming/scripting languages to enhance automation and REST/Graph APIs to create, maintain, and automate reporting solutions. Engage in migration opportunities from current supported technologies to cloud based solutions such as AWS cloud hosted solutions. Create documentation and standards for what "best practices in designing and developing data architecture" means. Can develop an executive-ready data architecture project, report or presentation with little instruction. Develops data architecture in support of KPIs reporting and analysis for their area of the business Lead planning, building and running enterprise class information management solutions across a variety of technologies (e.g. big data, master data, data profiling, batch processing, data modeling, and data indexing technologies,). Ensure appropriate policies are followed for information access, analysis, and dissemination. Responsible for the analysis, design, deployment, maturity, and support of data solutions. Define and apply information quality and consistency business rules throughout the data processing lifecycle. Collaborate with information providers to ensure ...

🌟 Data Analyst vs. Data Scientist vs. Data Engineer 🌟 Choosing a career in data involves understanding the distinct roles on a data team. Here’s a quick overview: 🔍 Data Analyst: Collects, cleans, and analyzes data to help businesses make informed decisions. They use statistical tools to interpret data and communicate findings to stakeholders. 🧑🔬 Data Scientist: Develops data-driven solutions using advanced analytics and machine learning. They focus on predictive modeling to solve business challenges and enhance products. 🛠️ Data Engineer: Builds and maintains data infrastructure, ensuring data is accessible and secure. They create data pipelines and systems that support analytics and modeling efforts. Key Differences 🎓 Education: - Data Analyst: Typically holds a Bachelor’s in fields like Mathematics or Economics. - Data Scientist: Often has a Master’s or Ph.D. in Statistics or Machine Learning. - Data Engineer: Usually has a degree in Engineering or Computer Science. 🛠️ Skills: - Data Analyst: Strong analytical skills and proficiency in data visualization tools. - Data Scientist: Expertise in machine learning and advanced statistical techniques. - Data Engineer: Proficient in coding, database management, and IT infrastructure. 📈 Career Paths: - Data Analyst: Progresses to roles like Senior Data Analyst or Data Science Manager. - Data Scientist: Advances to Senior Data Scientist or Chief Data Officer. - Data Engineer: Moves up to Data Architect or Manager of Data Engineering. Each role plays a vital part in transforming businesses into data-driven organizations. Whether you're interested in data analysis, data science, or data engineering, there’s a path for you. 🚀 #DataAnalytics #DataScience #DataEngineering #MachineLearning #CareerDevelopment #TechCareers #DataDriven #BigData #Analytics #STEM #CareerGrowth #DataAnalyst #DataScientist #DataEngineer #TechIndustry #FutureOfWork

Struggling to Find the Right Data Engineer? 📊 With #dataengineers in high demand, finding the right candidate can be challenging. checkout the latest blog that offers a comprehensive guide to streamlining your recruitment process. Explore essential insights such as: 👉 Key qualities to seek in a data engineer 👉 Crafting an appealing job description 👉 Effective candidate screening strategies 👉 Interview questions for evaluating technical skills Ready to build an exceptional data team? Click 👇to read more.. #dataengineering #recruitment #hiring #datascience #bigdata

Hello LinkedIn Members, I am looking to engage with the following people (Based in SA). Data Architects with 10 years experience + Data Integration specialists  + Data Analyst specialists in Governance and Data Engineers. Contract for 12 months. Remote (Based in SA). Please inbox me or e-mail [email protected] 😎 #DataScience #BigData #MachineLearning #DataAnalytics #ArtificialIntelligence #AI #DeepLearning #DataVisualization #DataDriven #DataMining #DataEngineering #PredictiveAnalytics #DataScientist #DataStrategy #BusinessIntelligence #DataOps #DataGovernance #DataQuality #DataIntegration #Analytics