Staff Software Engineer, Data Systems & Performance

StratusGrid is hiring a Staff Software Engineer to architect and build data systems optimization products and integrations for its Stratusphere platform. The role combines hands-on software development, data-system and cloud optimization, technical leadership, and collaboration with product, engineering, forward deployed, and customer teams.

Responsibilities:

• Technical Architecture: Drive architecture, interfaces, and engineering standards for data optimization products. Lead technical design for new features you develop, communicate tradeoffs, guide implementation, and design for expansion across clouds and data systems
• Agent-First Product Development: Use and oversee AI agents to build, test, and mature Stratusphere features, backend services, customer integrations, analysis tools, and user-facing workflows. Contribute production code and improve the platform's own data architecture, performance, and reliability
• Data Systems Optimization: Diagnose bottlenecks across queries, schemas, indexes, caching, data layout, compute, storage, and networking. Evaluate sizing, scaling, concurrency, and workload changes against customer performance, reliability, governance, and cost goals
• Customer Validation & Product Partnership: Work with FDEs, Product, and customer teams to identify opportunities, establish baselines, test hypotheses, and implement optimizations. Measure results, document tradeoffs, and turn repeatable findings into roadmap priorities, features, and integrations
• Engineering Judgment & Safe Automation: Take responsibility for AI-generated plans, code, queries, and recommendations. Ensure implementations are tested, maintainable, scalable, and secure, with appropriate access boundaries, change controls, auditability, and rollback
• Agent Improvement Feedback Loop: Identify recurring agent errors and missing context. Improve architecture, tooling, prompts, evaluation criteria, and workflows with Product and FDEs to measurably improve analysis quality and execution safety
• Add to the Foundation: Stay current with the latest methods and best practices for improving velocity, quality, and maintainability with agent-first development approaches. Share knowledge and actively contribute to maturing the processes, standards, and tooling that make the whole team better
• Cross-Functional Collaboration: Share context in visible channels, ask questions early, and work openly with Engineering, Product, FDEs, and customers. Surface assumptions and disagreements constructively so teams can learn quickly and make informed decisions
• Execution with Reliability & Urgency: Own work end to end, capture commitments, communicate progress proactively, escalate risks early, and close the loop. Follow change-control and customer experience standards, maintain clear documentation, and keep work records current

Requirements:

• Demonstrated ownership of architecture and implementation for complex production systems, with the ability to guide decisions across teams, influence through clear reasoning, and remain deeply hands-on
• A track record of improving complex data systems using profiling, query plans, observability, and representative benchmarks. Explain measured improvements in latency, throughput, reliability, or cost and the tradeoffs involved
• Deep relational database expertise and substantial optimization experience in at least one additional category: search, document or key-value systems, or analytical platforms. Strong SQL, data modeling, indexing, and distributed systems fundamentals
• Hands-on familiarity with Apache Iceberg and distributed query engines such as Athena, Starburst, or Trino. Understand partitioning, file sizing and compaction, metadata and statistics, and their effects on performance and cost
• Deep proficiency in at least one programming language and the ability to contribute across backend services, APIs, integrations, and frontend workflows. Learn new languages, frameworks, and data systems quickly and critically review AI-assisted work across the stack
• Architect services for layered testing and build automated delivery checks that catch defects early. Validate correctness, performance, and failure behavior while developing rapidly with AI agents
• Integrate and use metrics, logs, traces, and data-system diagnostics to investigate complex problems, establish baselines, and verify improvements in production
• Experience working in production cloud environments and managing infrastructure with infrastructure as code (IaC)
• Use AI and coding agents effectively for analysis, planning, implementation, and testing. Take ownership of AI-assisted work and remain accountable for its quality, security, and cost
• Translate complex findings into clear technical and business decisions. Listen to customer concerns, explain risks and tradeoffs with empathy, and turn incomplete information into actionable next steps
• Operate effectively in a fast-paced startup with evolving priorities and incomplete requirements. Identify the most valuable problems, make pragmatic decisions, test assumptions, and deliver in useful increments while maintaining engineering quality and customer trust
• Take initiative without waiting for detailed direction, meet commitments, adapt priorities thoughtfully, and communicate status and risks early. Balance delivery speed with reliability, maintainability, and follow-through
• Applicants must be legally authorized to work in the United States at the time of hire
• Must be eligible and able to meet applicable customer requirements for working in GovCloud and CJIS-regulated environments, including required background screening, security training, and access restrictions
• Databricks experience is strongly preferred, including Spark, Delta Lake, and workload optimization
• Experience with other lakehouse and analytical platforms, such as Snowflake, Microsoft Fabric, Azure Synapse Analytics, Google BigQuery, or Amazon Redshift, is also valuable
• TypeScript, React/Next.js, and Python; caching systems and strategies; and experience building reusable optimization tooling or customer-facing data products
• Experience building high-quality, cost-effective data pipelines with tools such as dbt, together with strong DevOps practices and familiarity with policy as code
• Familiarity with graph data models and databases, vector search, and retrieval-augmented generation (RAG), including performance and retrieval-quality tradeoffs