Staff Designated Support Engineer

Databricks; San Francisco (CA); On-site; USD 141,000 - 251,000; Full time

Job summary

A leading company in data and AI is seeking a Staff Designated Support Engineer to provide specialized support for their largest customers. This role involves troubleshooting complex issues with Spark and data technologies, collaborating with engineering teams, and enhancing customer experience through tailored technical solutions. Ideal candidates will have extensive experience in Big Data, Spark, and customer-facing roles, demonstrating strong problem-solving and collaboration skills.

Qualifications

• 8–12 years of experience in designing and troubleshooting distributed applications.
• 4+ years delivering production Spark/ML/AI solutions using Python, Java, or Scala.
• 3–5 years in customer-facing roles like Technical Account Manager or Solutions Architect.

Responsibilities

• Perform advanced troubleshooting and root cause analysis for Spark and Databricks features.
• Build rapid POCs and monitor solutions to address customer challenges.
• Train customer teams on best practices in performance tuning and debugging.

Skills

Big Data

Spark

Data Engineering

Problem Solving

Collaboration

Tools

AWS

Azure

GCP

CI/CD

Data Lakes

SQL

Snowflake

Redshift

BigQuery

Job description

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Job Location: San Francisco Bay Area, CA office with 50% travel to client location in San Francisco.

As a Sr. Staff Technical Solutions Engineer and technical 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 expertise in Apache Spark and data technologies to troubleshoot and resolve complex product issues, unblocking our customers’ 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 customer environments.
• Build rapid POCs, test, deploy, and monitor 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 leveraging Databricks features.
• Pilot new best practices, champion process improvements, and collaborate with cross-functional teams to enhance customer experience.
• Act as a trusted advisor in business review meetings, maintaining close relationships as a primary technical 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.

What We Look For

• Technical expertise in Big Data and Spark: 8–12 years of experience designing, building, and troubleshooting distributed applications, with 4+ years delivering production Spark/ML/AI solutions using Python, Java, or Scala.
• Data engineering specialization: Hands-on experience with Data Lakes, SQL databases, and cloud-based Data Warehousing/ETL tools like Snowflake, Redshift, BigQuery, etc.
• Advanced technical skills: Deep knowledge of Spark internals, Delta/Iceberg, JVM optimization, and memory management, with proficiency in AI ecosystems like Machine Learning, Deep Learning, and Generative AI.
• Cloud and CI/CD skills: Practical experience with AWS, Azure, or GCP, and expertise in building and managing CI/CD pipelines, monitoring, and alerting systems.
• Customer-facing experience: 3–5 years in roles like Technical Account Manager or Solutions Architect, demonstrating strong communication, relationship-building, and problem-solving skills.
• Proactive problem-solving skills: Proven ability to anticipate, identify, and mitigate risks, plan solutions for production challenges, and effectively coordinate team efforts.
• Collaboration and leadership: Ability to work with cross-functional teams and senior leadership to address roadblocks, mitigate risks, and drive customer success, while creating impactful self-service documentation.

Pay Range Transparency

Databricks is committed to fair compensation. The listed pay range is the base salary or on-target earnings, based on factors like skills, experience, certifications, and location. Actual packages may include bonuses, equity, and benefits. For details, visit our page here.

Local Pay Range: $141,700—$250,800 USD

About Databricks

Databricks is the data and AI company, trusted by over 10,000 organizations worldwide, including Fortune 500 companies. Headquartered in San Francisco, it was founded by the creators of Lakehouse, Apache Spark, Delta Lake, and MLflow. Follow us on Twitter, LinkedIn, and Facebook for updates.

Benefits

We offer comprehensive benefits. For regional details, visit https://www.mybenefitsnow.com/databricks.

Diversity & Inclusion

We foster a diverse, inclusive culture, considering all applicants without regard to protected characteristics.

Compliance

If access to export-controlled technology or source code is required, we may need to apply for U.S. government licenses, and may decline applications based on this requirement.

Additional Details

• Seniority level: Mid-Senior level
• Employment type: Full-time