[Hiring] Resident Solutions Architect - Digital Native Business @Databricks

Role Description

As a Big Data Solutions Architect (Resident Solutions Architect) in our Professional Services team, you will work with clients on short to medium term customer engagements on their big data challenges using the Databricks platform. You will provide data engineering, data science, and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers to get most value out of their data. RSAs are billable and know how to complete projects according to specification with excellent customer service. You will report to the regional Manager/Lead.

The impact you will have:

• Work on a variety of impactful customer technical projects which may include designing and building reference architectures, creating how-to's and productionalizing customer use cases.
• Work with engagement managers to scope variety of professional services work with input from the customer.
• Guide strategic customers as they implement transformational big data projects, 3rd party migrations, including end-to-end design, build and deployment of industry-leading big data and AI applications.
• Consult on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks.
• Provide an escalated level of support for customer operational issues.
• Work with the Databricks technical team, Project Manager, Architect and Customer team to ensure the technical components of the engagement are delivered to meet customer's needs.
• Work with Engineering and Databricks Customer Support to provide product and implementation feedback and to guide rapid resolution for engagement specific product and support issues.

Qualifications

• 6+ years experience in data engineering, data platforms & analytics.
• Comfortable writing code in either Python or Scala.
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one.
• Deep experience with distributed computing with Apache Spark™ and knowledge of Spark runtime internals.
• Familiarity with CI/CD for production deployments.
• Working knowledge of MLOps.
• Design and deployment of performant end-to-end data architectures.
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.
• Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
• Travel to customers 20% of the time.
• Databricks Certification.

Requirements

• 9+ years experience in data engineering, data platforms & analytics.
• Comfortable writing code in either Python or Scala.
• Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one.
• Deep experience with distributed computing with Apache Spark™ and knowledge of Apache Spark™ runtime internals.
• Familiarity with CI/CD for production deployments.
• Working knowledge of MLOps.
• Capable of design and deployment of highly performant end-to-end data architectures.
• Experience with technical project delivery - managing scope and timelines.
• Documentation and white-boarding skills.
• Experience working with clients and managing conflicts.

Benefits

• At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.
• For specific details on the benefits offered in your region, please visit here .

Company Description

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.