Support Engineer (AWS & IA)
- +3 years of experience in cloud operations, application or data platform support, SRE, or DevOps, with Databricks and AWS as the main platforms.
- Hands-on experience with Databricks jobs and Workflows, clusters, SQL warehouses, Unity Catalog, Spark logs, Spark UI, and familiarity with the bronze, silver, and gold medallion pattern.
- Strong knowledge of SQL, plus Python for debugging and scripting, and AWS services including Lambda, DynamoDB, Cognito, CloudWatch, IAM, and S3.
- Practical experience with integration and API troubleshooting, including logs, traces, browser developer tools, and understanding how React front ends interact with backend APIs.
- Working knowledge of GenAI and LLM applications, including prompts, RAG, tool or function calling, agents, production failure patterns, and AI-assisted coding or operations tools.
- Experience with ITIL-aligned incident and request management, including priority matrices, SLAs, escalation paths, and root cause analysis.
- Clear written English and a service-minded approach, particularly for tickets, runbooks, end-user communication, and white-glove support for business users.
- Additional relevant exposure to Databricks Apps, Lakebase, Delta Live Tables or Lakeflow, Databricks Asset Bundles, GitHub Actions, Terraform, ServiceNow, MCP-based integrations, Semantic Kernel, LangChain, LLM observability, Databricks AI features, SRE practices, or regulated industries is considered beneficial.
- Monitor Databricks jobs, workflows, data pipelines, Oracle API ingestion processes, and AWS services, responding to failures, alerts, performance issues, and data quality problems
- Own incident and service request resolution within defined SLAs, performing approved remediation such as job reruns, cluster restarts, configuration updates, and resolution of known data issues
- Collaborate with internal and external stakeholders and L3 product teams to troubleshoot end-to-end integrations, escalate complex or code-level issues with clear evidence, and validate implemented fixes
- Troubleshoot front-end, API, backend, database, connectivity, and integration issues using logs, traces, Spark UI, browser developer tools, monitoring tools, and system diagnostics
- Manage Databricks access and permissions across workspaces, Unity Catalog, groups, and service principals while supporting applications and data platforms
- Use AI-powered engineering tools such as GitHub Copilot and Cursor to accelerate troubleshooting, scripting, log analysis, and operational documentation
- Develop automation scripts, operational tooling, runbooks, knowledge articles, known-error documentation, and repeatable processes that reduce manual support effort
- Monitor platform health, performance, capacity, cost trends, dashboards, and observability while contributing to root cause analysis, service reviews, backlog grooming, and continuous improvement activities
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