Staff Technical Solutions Engineer, Data & AI
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead technical solutions for complex customer issues involving production data pipelines, AI workflows, and streaming ingestion. You will analyze customer code and architecture, mitigate critical incidents, advise stakeholders, influence product improvements, and mentor others in Data and AI production systems.
Requirements
- At least 8 years of experience designing, building, testing, and maintaining data pipelines
- Relevant Spark experience
- Expertise in SQL databases or data warehouses
- Expertise in ETL technologies
- At least 5 years of hands-on experience developing two or more Big Data technologies
- Around 3 years of hands-on experience as a technical lead on a Data and AI Engineering team
- Ability to influence cross-functionally with internal stakeholders or engineering teams
- Hands-on experience with AWS, Azure, or GCP is preferred
- Technical degree or Bachelor's degree in Computer Science or a related field or equivalent experience
Responsibilities
- Lead technical solutions for complex and escalated customer problems
- Perform code-level and systems-architecture analysis of customer workloads
- Resolve issues involving Spark internals, Spark SQL, Delta, DLT, and Model Serving
- Advise customer stakeholders during incidents and identify mitigations for advanced use cases
- Help prevent future customer issues and improve production stability
- Influence Engineering and Backline Support teams on product improvements
- Mentor others in the Databricks platform and production systems