Databricks
About this role
This is the staff level version of the designated support role, attached to Databricks' largest and most strategic customers in its Digital Native Business segment. A designated engineer is a named expert assigned to specific accounts, so the work combines deep troubleshooting with a long running relationship. You would perform advanced root cause analysis on performance and reliability problems across Spark, SQL, Delta, Streaming and the Databricks runtime, using Spark UI metrics, DAGs and event logs, then build rapid proofs of concept to unblock customers. You also write the playbooks and knowledge base entries that scale that expertise, train customer engineering and business teams on performance tuning and debugging, and represent the customer in business reviews. The posting asks for two separate kinds of experience: 8 to 12 years of distributed computing work, and 3 to 5 years in customer facing roles such as technical account management or solutions architecture.
Who this is for
What the posting requires:
- 8 to 12 years designing, building and troubleshooting distributed computing applications, with 4+ years delivering production scale Spark, ML or AI solutions in Python, Java or Scala.
- 3 to 5 years in customer facing roles such as Technical Account Manager or Solutions Architect, with strong communication and relationship building.
- Hands on expertise with data lakes, SQL based databases, and cloud data warehousing or ETL tools such as Snowflake, Redshift or BigQuery.
- Deep knowledge of Spark core internals, Delta or Iceberg, JVM optimisation and memory management.
- Proficiency in AI ecosystems including machine learning, deep learning and generative AI.
- Practical experience with AWS, Azure or GCP, plus building and managing CI/CD pipelines, monitoring and alerting.
- Proven ability to anticipate, identify and mitigate risk while planning solutions for production problems.
- Proven ability to work with cross functional teams and senior leadership.
What the work actually looks like:
- Advanced troubleshooting and root cause analysis on performance and reliability issues across Spark, SQL, Delta, Streaming and Databricks runtime features, using Spark UI metrics, Mosaic AI Model Service, DAGs and event logs.
- Work with R&D and network operations teams to define continuous monitoring that catches performance problems early in customer environments.
- Build rapid proofs of concept, then test, deploy and monitor solutions.
- Develop playbooks and maintain a knowledge base of common issues and solutions for Spark, ML and AI workflows.
- Train customer engineering and business teams on performance tuning, debugging and platform features.
- Advocate for customers in business review meetings as their trusted advisor and primary technical contact.
- Work onsite with field engineering, sales and product teams during customer engagements and technical presentations.
How this differs from the senior version of the same role, which is also on this board from an earlier edition: the staff posting raises the distributed computing requirement from 5 to 8 years up to 8 to 12, while keeping the same 3 to 5 years of customer facing experience. If you are between those bands, apply to the senior one.
Location and working pattern: Bengaluru, with onsite collaboration during customer engagements.
Compliance note in the posting: the standard Databricks export controlled technology clause applies.
Honest fit guidance: both halves are required. Deep Spark internals without customer facing experience, or account skill without the ability to read an event log, will each fall short.
- 8 to 12 years designing, building and troubleshooting distributed computing applications, with 4+ years delivering production scale Spark, ML or AI solutions in Python, Java or Scala.
- 3 to 5 years in customer facing roles such as Technical Account Manager or Solutions Architect, with strong communication and relationship building.
- Hands on expertise with data lakes, SQL based databases, and cloud data warehousing or ETL tools such as Snowflake, Redshift or BigQuery.
- Deep knowledge of Spark core internals, Delta or Iceberg, JVM optimisation and memory management.
- Proficiency in AI ecosystems including machine learning, deep learning and generative AI.
- Practical experience with AWS, Azure or GCP, plus building and managing CI/CD pipelines, monitoring and alerting.
- Proven ability to anticipate, identify and mitigate risk while planning solutions for production problems.
- Proven ability to work with cross functional teams and senior leadership.
What the work actually looks like:
- Advanced troubleshooting and root cause analysis on performance and reliability issues across Spark, SQL, Delta, Streaming and Databricks runtime features, using Spark UI metrics, Mosaic AI Model Service, DAGs and event logs.
- Work with R&D and network operations teams to define continuous monitoring that catches performance problems early in customer environments.
- Build rapid proofs of concept, then test, deploy and monitor solutions.
- Develop playbooks and maintain a knowledge base of common issues and solutions for Spark, ML and AI workflows.
- Train customer engineering and business teams on performance tuning, debugging and platform features.
- Advocate for customers in business review meetings as their trusted advisor and primary technical contact.
- Work onsite with field engineering, sales and product teams during customer engagements and technical presentations.
How this differs from the senior version of the same role, which is also on this board from an earlier edition: the staff posting raises the distributed computing requirement from 5 to 8 years up to 8 to 12, while keeping the same 3 to 5 years of customer facing experience. If you are between those bands, apply to the senior one.
Location and working pattern: Bengaluru, with onsite collaboration during customer engagements.
Compliance note in the posting: the standard Databricks export controlled technology clause applies.
Honest fit guidance: both halves are required. Deep Spark internals without customer facing experience, or account skill without the ability to read an event log, will each fall short.
Apply on company site
Opens databricks.com, the employer's own application page. Applying is always free.