Specialist Solutions Architect

Навыки

• AWS
• Azure
• BI-инструменты
• Big Data
• CI/CD
• Data Lake
• Отладка и поиск ошибок

О компании и продукте

• Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram .

Задачи

• Provide technical leadership to guide strategic customers to successful implementations on big data projects, ranging from architectural design to data engineering to model deployment
• Architect production level workloads, including end-to-end pipeline load performance testing and optimisation
• Provide technical expertise in an area such as data management, cloud platforms, data science, machine learning, or architecture
• Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing, and custom architectures
• Improve community adoption (through tutorials, training, hackathons, conference presentations)
• Contribute to the Databricks Community
• FEQ227R130
• As a Specialist Solutions Architect (SSA), you will guide customers in building big data solutions on Databricks that span a large variety of use cases

Требования

• You will have experience in a customer-facing technical role with expertise in at least one of the following
• Software Engineer/Data Engineer: query tuning, performance tuning, troubleshooting, and debugging Spark or other big data solutions
• Data Scientist/ML Engineer: model selection, model lifecycle, hyper parameter tuning, model serving, deep learning
• Data Applications Engineer: Build use cases that use data - such as risk modelling, fraud detection, customer life-time value
• Experience with design and implementation of big data technologies such as Spark/Delta, Hadoop, NoSQL, MPP, OLTP, and OLAP
• Maintain and extend production data systems to evolve with complex needs
• Production programming experience in Python, R, Scala or Java
• Deep Specialty Expertise in at least one of the following areas
• Experience scaling big data workloads that are performant and cost-effective
• Experience with Development Tools for CI/CD, Unit and Integration testing, Automation and Orchestration, REST API, BI tools and SQL Interfaces
• Experience designing data solutions on cloud infrastructure and services, such as AWS, Azure, or GCP using best practices in cloud security and networking
• Experience with ML concepts covering Model Tracking, Model Serving and other aspects of productionizing ML pipelines in distributed data processing environments like Apache Spark, using tools like MLflow
• Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Native level Korean is required and Business level English is a plus
• These are customer-facing roles, working with and supporting the Solution Architects, requiring hands-on production experience with Apache Spark™ and expertise in other data technologies
• SSAs help customers through design and successful implementation of essential workloads while aligning their technical roadmap for expanding the usage of the Databricks Lakehouse Platform
• As a deep go-to-expert reporting to the Director of Field Engineering, you will continue to strengthen your technical skills through mentorship, learning, and internal training programs and establish yourself in an area of specialty - whether that be performance tuning, machine learning, industry expertise, or more

Условия

• At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees

Паспорт вакансии

История публикации

Откуда что взялось

Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.

Почему на этом месте в выдаче

Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.