| Analytics Cloud Engineer || at Remote, Remote, USA |
| Email: [email protected] |
| From: Ishavdeep Singh, Cloud Think Technologies [email protected] Reply to: [email protected] Job Description: Designing and building a robust data science platform for machine learning operations (ML-Ops) is a huge challenge as our client transitions from on-prem to cloud infrastructure. This position is an opportunity for an experienced cloud engineer to develop expertise in implementing modern data science capabilities. You will be working with a team of technical data scientists and cloud engineers to build out our new cloud-based data science platform. Many of the core capabilities are already developed and being used. Our client is now focused on their data landscape, bringing together data virtualization, data mesh, and data federation capabilities. Alongside data access, they need to implement modern data catalog and data governance systems. The candidate hired for this position would support vetting new tools and services, implementing concepts, and developing best practices and training. The role will require understanding our users needs, learning the workings of our client's disparate data warehouses, vetting, and developing appropriate and cost-effective tools. The job is RIGHT for you if ... You want to build a career in AI/Client solutions. Learning different technologies and staying current with emerging capabilities will require self-motivation and discovery. You want to conduct hands-on work. While thinking strategically and drafting theoretical solutions is part of the job, you will be expected to write code, test solutions, and develop boilerplate processes. You do not like to work independently. This position will work closely with different teams from different organizations. They will work in a very agile environment and be required to be an active team member. You become demotivated by work that is comfortable and predictable. Resilience is key. In this agile environment there will be many pivots in strategy. You must be willing to quickly shift from one responsibility to another, learn new skills, and adapt. Responsibilities: Develop and implement technical efforts to design, build, and deploy Azure applications, including large-scale data processing, data virtualization technology, and advanced analytics Translate requirements from technical and non-technical data scientists to appropriate cloud engineering requirements Interact with data storage technologies such as relational databases, NoSQL, and data lakes Vet and deploy cloud-based technologies such as data mesh, data catalog, and automatic governance systems Design and develop workflows to automate the deployment of applications and infrastructure environments Connect systems to remote data warehouses for data extraction using API and JDBC Connectors Apply engineering rigor and best practices to machine learning, including CI/CD, automation, etc. Research and explore new technology patterns and frameworks, implement standalone reference applications, or apply to existing problems Troubleshoot incidents, identify root causes, fix, and document problems, and implement preventive measures Collaboratively interface with teams to understand technology strategy, including new applications being authored, and backlog of enhancements to existing applications Educate teams on the implementation of new cloud-based initiatives, providing associated training when necessary Demonstrate exceptional problem-solving skills, with an ability to see and solve issues before they affect business productivity Work with new and existing vendors, gathering industry intelligence, and bringing in expertise from external parties Qualifications: Cloud experience Azure is highly preferred. Particularly understanding infrastructure as code designs, data virtualization, and data cataloguing. Proficiency with Python Three or more years of experience in architecting, designing, developing, and implementing cloud solutions on cloud platforms (Azure preferred) Experience building end-to-end systems as a Platform Engineer, Client DevOps Engineer, or Data Engineer (or equivalent) Experience in several of the following areas: database architecture, ETL, business intelligence, big data, machine learning, advanced analytics Proven ability to collaborate with multidisciplinary teams of business analysts, developers, data scientists, and subject-matter experts Experience implementing data virtualization technologies Experience working with cloud computing and database systems Experience managing cloud storage technologies (blob, table, file, queues, and services) Strong software engineering skills in complex, multi-language systems Preferred experience with Python, Docker, Terraform, Purview, Azure Data Factory, and other Azure services Experience building custom integrations between cloud-based systems using APIs Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.) Strong understanding of software testing, benchmarking, and continuous integration Ability to translate business needs to technical requirements Education & Experience Bachelors or masters degree in computer science, information technology, or mathematics 3+ years experience building production-quality software. Knowledge of web services, API, REST, and RPC Cloud certification preferred (Azure, AWS, GCP) Keywords: continuous integration continuous deployment artificial intelligence machine learning |
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| Fri Mar 15 20:35:00 UTC 2024 |