Global Mobility Services’ Post

Please find the below JD and share me resume with murali@meritore.com Need local to MA Only No H1B Job Title : Senior Data Engineer Location : Quincy, MA Education Qualifications: Minimum Qualifications • Technical Expertise: 8+ years in data engineering with strong skills in Python, PySpark, SQL, and extensive, hands-on experience with Databricks and big data frameworks. Expertise in integrating data science workflows and deploying ML models for real-time and batch processing within a cybersecurity context. • Cloud Proficiency: Advanced proficiency in AWS, including EC2, S3, Lambda, ELB, and container orchestration (Docker, Kubernetes). Experience in managing large-scale data environments on AWS, optimizing for performance, security, and compliance. • Security Integration: Proven experience implementing SCAS, SAST, DAST/WAS, and secure DevOps practices within an SDLC framework to ensure data security and compliance in a high- stakes cybersecurity environment. • Data Architecture: Demonstrated ability to design and implement complex data architectures, including data lakes, data warehouses, and lake house solutions. Emphasis on secure, scalable, and highly available data structures that support ML-driven insights and real-time analytics. • Data Quality ; Governance: Hands-on experience with automated data quality checks, data lineage, and governance standards. Proficiency in Databricks DQM or similar tools to enforce data integrity and compliance across pipelines. • Data Analytics; Visualization: Proficiency with analytics and visualization tools such as Databricks, Power BI, and Tableau to generate actionable insights for cybersecurity risks, threat patterns, and vulnerability trends. Skilled in translating complex data into accessible visuals and reports for cross-functional teams. • CI/CD and Automation: Experience building CI/CD pipelines that automate testing, security scans, and deployment processes. Proficiency in deploying ML models and data processing workflows using CI/CD, ensuring consistent quality and streamlined delivery. • Agile Experience: Deep experience in Agile/Scrum environments, with a thorough agility and cross-functional collaboration. Preferred Experience: • Advanced Data Modeling; Governance: Expertise in designing data models for cybersecurity data analytics, emphasizing data lineage, federation, governance, and compliance. Experience ensuring security and privacy within data architectures. • Machine Learning; Predictive Analytics: Experience deploying ML algorithms, predictive models, and anomaly detection frameworks to bolster CASM platform’s cybersecurity capabilities. • High-Performance Engineering Culture: Background in mentoring engineers in data engineering best practices, promoting data science, ML, and analytics integration, and fostering a culture of collaboration and continuous improvement.

Please find the below JD and share me resume with murali@meritore.com Need local to MA Only No H1B Job Title : Senior Data Engineer Location : Quincy, MA Education Qualifications: Minimum Qualifications • Technical Expertise: 8+ years in data engineering with strong skills in Python, PySpark, SQL, and extensive, hands-on experience with Databricks and big data frameworks. Expertise in integrating data science workflows and deploying ML models for real-time and batch processing within a cybersecurity context. • Cloud Proficiency: Advanced proficiency in AWS, including EC2, S3, Lambda, ELB, and container orchestration (Docker, Kubernetes). Experience in managing large-scale data environments on AWS, optimizing for performance, security, and compliance. • Security Integration: Proven experience implementing SCAS, SAST, DAST/WAS, and secure DevOps practices within an SDLC framework to ensure data security and compliance in a high- stakes cybersecurity environment. • Data Architecture: Demonstrated ability to design and implement complex data architectures, including data lakes, data warehouses, and lake house solutions. Emphasis on secure, scalable, and highly available data structures that support ML-driven insights and real-time analytics. • Data Quality ; Governance: Hands-on experience with automated data quality checks, data lineage, and governance standards. Proficiency in Databricks DQM or similar tools to enforce data integrity and compliance across pipelines. • Data Analytics; Visualization: Proficiency with analytics and visualization tools such as Databricks, Power BI, and Tableau to generate actionable insights for cybersecurity risks, threat patterns, and vulnerability trends. Skilled in translating complex data into accessible visuals and reports for cross-functional teams. • CI/CD and Automation: Experience building CI/CD pipelines that automate testing, security scans, and deployment processes. Proficiency in deploying ML models and data processing workflows using CI/CD, ensuring consistent quality and streamlined delivery. • Agile Experience: Deep experience in Agile/Scrum environments, with a thorough agility and cross-functional collaboration. Preferred Experience: • Advanced Data Modeling; Governance: Expertise in designing data models for cybersecurity data analytics, emphasizing data lineage, federation, governance, and compliance. Experience ensuring security and privacy within data architectures. • Machine Learning; Predictive Analytics: Experience deploying ML algorithms, predictive models, and anomaly detection frameworks to bolster CASM platform’s cybersecurity capabilities. • High-Performance Engineering Culture: Background in mentoring engineers in data engineering best practices, promoting data science, ML, and analytics integration, and fostering a culture of collaboration and continuous improvement.

Please find the below JD and share me resume with murali@meritore.com Need local to MA Only No H1B Job Title : Senior Data Engineer Location : Quincy, MA Education Qualifications: Minimum Qualifications • Technical Expertise: 8+ years in data engineering with strong skills in Python, PySpark, SQL, and extensive, hands-on experience with Databricks and big data frameworks. Expertise in integrating data science workflows and deploying ML models for real-time and batch processing within a cybersecurity context. • Cloud Proficiency: Advanced proficiency in AWS, including EC2, S3, Lambda, ELB, and container orchestration (Docker, Kubernetes). Experience in managing large-scale data environments on AWS, optimizing for performance, security, and compliance. • Security Integration: Proven experience implementing SCAS, SAST, DAST/WAS, and secure DevOps practices within an SDLC framework to ensure data security and compliance in a high- stakes cybersecurity environment. • Data Architecture: Demonstrated ability to design and implement complex data architectures, including data lakes, data warehouses, and lake house solutions. Emphasis on secure, scalable, and highly available data structures that support ML-driven insights and real-time analytics. • Data Quality ; Governance: Hands-on experience with automated data quality checks, data lineage, and governance standards. Proficiency in Databricks DQM or similar tools to enforce data integrity and compliance across pipelines. • Data Analytics; Visualization: Proficiency with analytics and visualization tools such as Databricks, Power BI, and Tableau to generate actionable insights for cybersecurity risks, threat patterns, and vulnerability trends. Skilled in translating complex data into accessible visuals and reports for cross-functional teams. • CI/CD and Automation: Experience building CI/CD pipelines that automate testing, security scans, and deployment processes. Proficiency in deploying ML models and data processing workflows using CI/CD, ensuring consistent quality and streamlined delivery. • Agile Experience: Deep experience in Agile/Scrum environments, with a thorough agility and cross-functional collaboration. Preferred Experience: • Advanced Data Modeling; Governance: Expertise in designing data models for cybersecurity data analytics, emphasizing data lineage, federation, governance, and compliance. Experience ensuring security and privacy within data architectures. • Machine Learning; Predictive Analytics: Experience deploying ML algorithms, predictive models, and anomaly detection frameworks to bolster CASM platform’s cybersecurity capabilities. • High-Performance Engineering Culture: Background in mentoring engineers in data engineering best practices, promoting data science, ML, and analytics integration, and fostering a culture of collaboration and continuous improvement.

𝐇𝐨𝐰 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚𝐧 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 ? =========================== Azure Data Engineers play a crucial role in designing and implementing cloud-based data solutions. Here’s a quick guide to getting started in this exciting field: 1. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞 Design and manage data pipelines, storage, and processing systems. Optimize performance and ensure data security. 2. 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐒𝐭𝐫𝐨𝐧𝐠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 Learn Python, SQL, and database management (relational and NoSQL). Understand data modeling, data warehousing, and big data concepts. 3. 𝐌𝐚𝐬𝐭𝐞𝐫 𝐀𝐳𝐮𝐫𝐞 𝐁𝐚𝐬𝐢𝐜𝐬 Start with Azure Fundamentals (AZ-900) to understand cloud services. 4. 𝐋𝐞𝐚𝐫𝐧 𝐊𝐞𝐲 𝐀𝐳𝐮𝐫𝐞 𝐓𝐨𝐨𝐥𝐬 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐅𝐚𝐜𝐭𝐨𝐫𝐲: Build data pipelines. 𝐀𝐳𝐮𝐫𝐞 𝐒𝐲𝐧𝐚𝐩𝐬𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Data warehousing and analytics. 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬: Big data processing and ML. 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Handle large-scale data. 5. 𝐆𝐞𝐭 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 Earn the Azure Data Engineer Associate (DP-203) certification to validate your skills. 6. 𝐆𝐚𝐢𝐧 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 Work on projects like building pipelines, designing data warehouses, and performing analytics using Azure tools. 7. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 Learn Azure RBAC, encryption, and governance tools. 8. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞 𝐘𝐨𝐮𝐫 𝐒𝐤𝐢𝐥𝐥𝐬 Explore data streaming, performance optimization, and ML integration. 9. 𝐋𝐚𝐧𝐝 𝐘𝐨𝐮𝐫 𝐑𝐨𝐥𝐞 Build a strong resume showcasing certifications and projects. Network through Azure forums and meetups. With dedication and hands-on practice, you’ll be ready to thrive as an Azure Data Engineer. Start building your skills today! 🚀 Tagging Arun Kumar for better reach. #ForumDE #DataEngineer #Azure

As the data landscape continues to evolve, the role of the Data Engineer has become increasingly crucial in unlocking the true potential of data-driven organizations. Data Engineers are responsible for building the infrastructure and pipelines that transform raw data into actionable insights, powering strategic decision-making. At the core of a Data Engineer's responsibilities lies a solid foundation in programming languages, database management, and operating systems. Proficiency in tools like Python, SQL, and Linux empowers Data Engineers to build robust data pipelines and harness the power of data. Beyond the technical expertise, effective communication, collaboration, and project management skills are essential for Data Engineers to navigate the intricate world of data engineering and collaborate seamlessly with cross-functional teams. Soft skills and real-world experience play a vital role in the success of data engineering projects. The data engineering ecosystem encompasses a wide range of processes and technologies, including data modeling, ETL (Extract, Transform, Load) workflows, and data warehousing. Mastering tools like Apache Airflow, Apache Spark, and Kafka equips Data Engineers to handle the growing volume, velocity, and variety of data. Moreover, the modern data engineering landscape is heavily influenced by cloud computing, infrastructure as code, and DevOps practices. Familiarity with cloud platforms, containerization, and CI/CD pipelines empowers Data Engineers to deliver scalable, reliable, and highly available data solutions. If you're aspiring to embark on a rewarding career in data engineering or looking to enhance your existing skillset, now is an exciting time to dive into this dynamic field. Embrace the opportunities presented by the ever-evolving world of data engineering and position yourself as a sought-after data professional. #DataEngineering #CareerDevelopment #TechSkills #DataScience #CloudComputing

🚀 Senior Data Engineer Available! 🚀 I'm excited to share that we have a highly skilled interim Senior Data Engineer who has just completed his role working with AWS at the University of Bath. This candidate comes with glowing recommendations and is now looking for his next interim opportunity. Candidate Highlights: University of Bath: - Designed and built an AWS-based ingestion framework for ETL pipelines. - Developed centralized data integration processes, including encryption, quality, and monitoring. - Created microservices to meet strategic goals, reduce costs, and improve reporting accuracy. - Utilized S3 Iceberg, Redshift, and PowerBI for enhanced data management. - Built a DevOps framework with CodeDeploy and CodePipeline. - Implemented CI/CD using Python CDK and CloudFormation. - Developed layered data frameworks and low-code options with Databrew and GlueDQ. Extensive Cloud Expertise: Proficient in AWS, Azure, and Cloudera, focusing on big data collection, profiling, and transformation. Security Cleared Professional: Experienced in deploying well-orchestrated pipelines for CRMs, Risk, AML, Financial Trading, and more. Technical Proficiency: Skilled in SQL, YAML, JSON, and Python (including PySpark), delivering production-quality code. Database Management: Expertise in data modelling, cataloguing, and processing across MongoDB, DynamoDB, PostgreSQL, MySQL, Redshift, and more. DevOps and CI/CD: Strong background in containerization and IaC using Docker, Kubernetes, Terraform, and GitHub Actions. Project Management: Experienced in Agile and Scrum, managing JIRA for sprints and backlogs. Looking for a dedicated and knowledgeable Data Engineer to elevate your university's data infrastructure? If you are interested in reviewing his profile or have any questions, please reach out at z.hinkinson@realstaffing.com #DataEngineering #AWS #Azure #CloudComputing #BigData #DevOps #CI/CD #ProjectManagement #StakeholderEngagement #SQL #Python #ETL #DataPipelines #UniversityTech #HigherEdTech #TechInnovation

⭐ Data Engineering Essentials: Building the Foundations of Data Excellence ⭐ 🚀 Exploring Career Paths in Data Engineering 📊 Data engineering is a dynamic field with numerous exciting career paths to explore! If you're passionate about building robust data solutions and leveraging cutting-edge technologies, here are some roles to consider: Data Engineer: Design, build, and maintain scalable data management systems. Big Data Engineer: Handle large volumes of data using technologies like Hadoop and Spark. Data Pipeline Engineer: Design and optimize data pipelines for smooth data flow. ETL Developer: Build and maintain ETL processes for data extraction, transformation, and loading. Data Architect: Design the overall structure of data systems and databases. Machine Learning Engineer: Apply data engineering principles to deploy machine learning models at scale. Cloud Data Engineer: Build data solutions on cloud platforms like AWS, GCP, and Azure. Streaming Data Engineer: Process and analyze real-time data streams using technologies like Kafka and Flink. DataOps Engineer: Automate and optimize the data lifecycle with DevOps principles. Data Governance Specialist: Define and enforce data management policies and standards. Each path offers unique opportunities for growth and impact. Which one resonates with you? Let's embark on an exciting journey in the world of data engineering! 🚀📊 #DataEngineering #CareerPath #TechJobs

🚀 The Three Phases of a Data Engineering Project and Key Roles Involved Data engineering projects typically unfold in three distinct phases, each involving specific teams and skill sets. Here's an overview of each phase and the roles crucial for success: 1. Pre-Implementation Phase Roles Involved: Sales/Commercial Team, Solution Architects, Data Engineers   Description:  In this phase, discussions focus on identifying the need for a data engineering project. In service-based companies, this is referred to as "pre-sales," whereas product-based companies have internal meetings with business and technical teams. Solution architects and data engineers collaborate to design the architecture and estimate costs. 2. Implementation Phase Roles Involved: DevOps/Infrastructure Engineers (Platform Engineers), Data Engineers, Project Managers   Description:   DevOps engineers set up the infrastructure using Infrastructure-as-Code (IaC) tools like Terraform or Bicep. After the infrastructure is ready, data engineers build the data platform and automate deployments with CI/CD pipelines. Project managers oversee development, ensuring the project stays on track with the budget and timeline. 3. Post-Implementation Phase (Maintenance Phase) Roles Involved: Data Engineers, Project Managers   Description:   This phase focuses on platform maintenance, issue resolution, and implementing new features. Data engineers debug pipeline failures, leveraging the documentation from the implementation phase. Project managers manage support tickets and coordinate ongoing work. 💡 Each phase is crucial for the project's success, and collaboration among these roles ensures a smooth delivery. #DataEngineering #ProjectManagement #Cloud #DevOps #DataPlatform #DataTeam #TechCareers #Azure #Terraform #CI_CD

Urgent requirements If interested share your resume shalini@jpctechno.com 1)Cloud Infrastructure Engineer, 2) Project Manager,  3) AWS Data Engineer Location: Phoenix, AZ W2 role 1)Cloud Infrastructure Engineer,    architecting, deploying, and maintaining cloud solutions. You will work with cross-functional teams to ensure the reliability, scalability, and security of our cloud infrastructure. Key Responsibilities: Cloud Architecture: Design and implement scalable and secure cloud infrastructure solutions on platforms such as AWS, Azure, or Google Cloud. Develop best practices for cloud architecture and governance. Deployment and Management: 2) Project Manager, planning, executing, and closing projects within specified timelines and budgets. You will work closely with cross-functional teams, stakeholders, and clients to ensure project goals are met and aligned with business objectives. Key Responsibilities: Project Planning: Define project scope, objectives, and deliverables. Develop detailed project plans, including timelines, resource allocation, and budgets. Identify and manage project risks and issues proactively. Execution and Monitoring: Lead project teams to ensure successful execution of project plans. Monitor project progress, track milestones, and make adjustments as needed. Prepare and present regular status reports to stakeholders. 3) AWS Data Engineer  designing, developing, and maintaining data pipelines and data processing systems in the AWS environment. You will work closely with data architects, data analysts, and business stakeholders to ensure the efficient handling and utilization of data. Key Responsibilities: Data Pipeline Development: Design, develop, and maintain ETL processes to ingest and transform data from various sources into data warehouses or data lakes. Utilize AWS services (e.g., AWS Glue, Lambda, Kinesis) to build robust data pipelines. Data Modeling and Storage: Implement effective data models to support analytics and reporting. Optimize data storage solutions using AWS technologies such as Redshift, S3, and RDS. Performance Optimization: Monitor and optimize data processing and storage performance. Troubleshoot and resolve data pipeline issues to ensure data integrity and availability.

#hiring Information Technology - Data Integration Engineer, Raleigh, United States, fulltime #jobs #jobseekers #careers #Raleighjobs #NorthCarolinajobs #ITCommunications Apply: https://lnkd.in/gZ5cCwB9 Data Integration Engineer12+ Months Contract 100% RemoteSkills Required: Looking for an engineer that has knowledge on Pipelines, azure devops pipeline work. How to manage database backups and restore Doing a lot of migration of databases for all the projects. Assigned into a project and each project has different stages - some of them are provisioning users, Needs to understand how authentication works. Integration and preparation - understanding how file transfers work and how SFTP get set up. Mainly data and file transfer protocols Database manipulations - receive database and run a series of pipelines to manipulate the data and add it into a configuration for the specific project they are working on. Follow up with necessary people. Adding users, refreshing the database 2-3times during project Maintaining the tasks during the project until they can take it live. Be preparing the script during the go live. Running pipelines, creating some SQL scripts to update information for the user, customer. Knowledge of Azure, SQL, Azure Databases, Azure Concepts, TSQL, Microsoft SQL language to Query. Knowledge of how to upload data into the cloud. Query or troubleshoot pipeline in ADO/Azure Devops. Write pipeline files. Understand how a cloud application works and will set them apartManaging cloud applications.