Job description

P-1011 Job Location: San Francisco Bay Area, CA As a Sr. Staff Technical Solutions Engineer and tech subject matter expert, you will partner closely with our Field and Engineering teams to deliver high-touch specialized support and tailored technical solutions for Databricks' largest and most strategic customers in the Digital Native Business (DNB) segment. In this customer-facing role, you will leverage your technical expertise in Apache Spark™ and other data technologies to triage and resolve complex product issues and unblock our customers’ most critical technical challenges. The Impact You Will Have Perform advanced Troubleshooting and Root Cause Analysis to resolve performance and reliability issues in Spark, SQL, Delta, Streaming, and Databricks runtime features using tools like Spark UI metrics, Mosaic AI Model Service, DAGs, and event logs. Discover requirements for continuous monitoring to detect early performance issues working with R&D and NOC teams to optimize the DNB customer environments. Build Rapid POCs, Test/Deploy/Monitor the solutions built by Databricks Engineering to address customer challenges and showcase advanced Spark/ML/AI runtime capabilities aligned with their business goals. Develop comprehensive playbooks and maintain a knowledge base of common issues and solutions for Spark, ML, and AI workflows. Train customer engineering and business teams on best practices in performance tuning, debugging, and effectively leveraging Databricks Features. Pilot new best practices processes/ programs, champion process improvements, and collaborate with cross-functional teams to enhance the customer experience. Advocate for customers in business review meetings and maintain close relationships as a trusted advisor and primary technical point of contact. Collaborate onsite with Field Engineering, Sales, and Product teams during customer engagements and technical presentations to provide rapid solutions to production-impacting issues, demonstrating deep technical expertise and building strong customer trust. This role requires approximately 20% travel What We Look For Technical Expertise in Big Data and Spark: 8–12 years of experience designing, building, and troubleshooting distributed computing applications, with 4+ years delivering production-scale Spark/ML/AI solutions using Python, Java, or Scala. Data Engineering Specialization: Hands-on expertise with Data Lakes, SQL-based databases, and Cloud-based Data Warehousing/ETL tools like Snowflake, Redshift, Bigquery, etc Advanced Tech Skills : Deep knowledge of Spark core internals, Delta/Iceberg, JVM optimization, and memory management, with additional proficiency in AI ecosystems like Machine Learning, Deep Learning, and Generative AI. Cloud and CI/CD Skills: Practical experience with AWS, Azure, or GCP, coupled with expertise in building and managing CI/CD pipelines, monitoring, and alerting systems. Customer-Facing Experience: 3–5 years in customer-facing roles such as Technical Account Manager or Solutions Architect, demonstrating strong communication, relationship-building, and problem-solving skills. Advanced Proactive Problem Solving Skills : Proven ability to anticipate, identify, and mitigate risks while planning solutions for production challenges. Effectively use sound business judgment, risk avoidance and subject matter expert resources to coordinate team efforts to solve problems. Collaboration and Leadership: Proven ability to work with cross-functional teams and senior leadership to address roadblocks, mitigate risks, and drive customer success while creating impactful documentation for self-service solutions. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $141,700 — $250,800 USD About Databricks 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 . 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 click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

Requirements

Must-haves

Job Location: San Francisco Bay Area, CA
The Impact You Will Have
Discover requirements for continuous monitoring to detect early performance issues working with R&D and NOC teams to optimize the DNB customer environments.
Build Rapid POCs, Test/Deploy/Monitor the solutions built by Databricks Engineering to address customer challenges and showcase advanced Spark/ML/AI runtime capabilities aligned with their business goals.
Develop comprehensive playbooks and maintain a knowledge base of common issues and solutions for Spark, ML, and AI workflows.
Train customer engineering and business teams on best practices in performance tuning, debugging, and effectively leveraging Databricks Features.
Pilot new best practices processes/ programs, champion process improvements, and collaborate with cross-functional teams to enhance the customer experience.
Advocate for customers in business review meetings and maintain close relationships as a trusted advisor and primary technical point of contact.
This role requires approximately 20% travel
What We Look For
Data Engineering Specialization: Hands-on expertise with Data Lakes, SQL-based databases, and Cloud-based Data Warehousing/ETL tools like Snowflake, Redshift, Bigquery, etc
Advanced Tech Skills : Deep knowledge of Spark core internals, Delta/Iceberg, JVM optimization, and memory management, with additional proficiency in AI ecosystems like Machine Learning, Deep Learning, and Generative AI.
Cloud and CI/CD Skills: Practical experience with AWS, Azure, or GCP, coupled with expertise in building and managing CI/CD pipelines, monitoring, and alerting systems.
Customer-Facing Experience: 3–5 years in customer-facing roles such as Technical Account Manager or Solutions Architect, demonstrating strong communication, relationship-building, and problem-solving skills.
Our Commitment to Diversity and Inclusion

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

sqlaimlpythonjavamachine learningci/cdawsazuregcp

Platform: greenhouse
Effort: quick