Senior Designated Support Engineer Apache Spark
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Maintainer signals as of 9/23/2026
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 provide high-touch support for strategic customer accounts. You will investigate complex Apache Spark and data engineering issues, manage support cases, coordinate with internal stakeholders, and help customers optimize their data pipelines and platform usage.
Requirements
- At least 3 years of data engineering experience building, deploying, and maintaining large-scale data pipelines
- At least 3 years of experience designing, developing, testing, and sustaining Python, Java, or Scala applications
- Deep architectural knowledge of Apache Spark internals
- Experience troubleshooting Spark and big-data issues including data skew, out-of-memory errors, serialization issues, and slow broadcast joins
- Project management experience across multiple tasks and workstreams
- Analytical and problem-solving skills in distributed big-data computing
- Written and verbal customer communication skills
- Customer-facing support engineering, technical account management, DSE, or production support experience preferred
Responsibilities
- Manage strategic accounts and provide high-touch support
- Investigate complex Apache Spark and data engineering issues
- Analyze Spark UI, thread dumps, and driver and executor logs
- Advise customers on ETL and ELT optimization, data modeling, and Lakehouse architecture
- Manage open support cases and expedite ticket closure
- Coordinate customer communications with Engineering, Product, subject-matter experts, and Account teams
- Resolve business-impacting situations and restore customer functionality
- Review customer cases to identify trends and optimization opportunities
- Advocate for customers and lead stakeholder roundtables
- Help customers use Databricks Support tools and processes independently