About the role
As a Sr. Solutions Architect on the AI native team supporting the Digital Natives business, you will shape the future of the big data landscape by working with the most sophisticated data engineering and data science teams in the world.
Reporting to the Field Engineering Manager, you will collaborate with customers, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide customers through the competitive landscape, best practices, and implementation, and develop technical champions along the way.
The impact you will have
- You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
- Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
- Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
- Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.
What we look for
- 7+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
- Experience building distributed data systems
- Comfortable programming in and debugging Python and SQL
- Have built solutions with public cloud providers such as AWS, Azure, or GCP
- Expertise in one of the following:
- Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
- Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, PyTorch, Tensorflow)
- Available to travel to customers in your region
- [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
- Nice to have: Databricks Certification
- Location: Remote (Pacific Time preferred; Mountain Time considered).
NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply. We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.