Staff Software Engineer, Data Systems & Performance

StratusGrid

2d•Remote

About The Position

StratusGrid is seeking a staff-level software engineer to shape the architecture and development of data systems optimization products and integrations, while improving the data systems behind its agentic platform, Stratusphere. The role works across data systems and cloud infrastructure, contributes production code, and leads technical design for features. In collaboration with product engineering, forward deployed engineers (FDEs), and customers, the engineer tests and implements optimizations in real environments and turns proven approaches into reusable platform capabilities. Customer workloads range from gigabytes to petabytes across AWS and Azure, with additional clouds planned. The position calls for broad systems expertise spanning relational databases, search and document or key-value systems, analytical platforms, and lakehouse systems. The current platform uses Apache Iceberg tables in Amazon S3 with Athena and Starburst. The engineer will use and oversee AI agents to develop and improve platform features, services, integrations, analysis tools, and workflows, while ensuring the resulting work is secure, reliable, maintainable, and appropriately tested. This is a remote role, with quarterly travel to the Chattanooga, Tennessee headquarters and possible travel up to monthly. Applicants must be legally authorized to work in the United States and eligible to meet applicable customer requirements for GovCloud and CJIS-regulated environments.

Requirements

Demonstrated staff-level ownership of architecture and implementation for complex production systems, with the ability to guide cross-team decisions, influence through clear reasoning, and remain hands-on.
A track record of improving complex data systems using profiling, query plans, observability, and representative benchmarks; ability to explain measured effects on latency, throughput, reliability, or cost and the associated tradeoffs.
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, including partitioning, file sizing and compaction, metadata, statistics, and their performance and cost effects.
Deep proficiency in at least one programming language and ability to contribute across backend services, APIs, integrations, and frontend workflows.
Ability to learn languages, frameworks, and data systems quickly and critically review AI-assisted work across the stack.
Ability to architect services for layered testing and build automated delivery checks; validate correctness, performance, and failure behavior while developing with AI agents.
Experience integrating and using metrics, logs, traces, and data-system diagnostics to investigate complex problems, establish baselines, and verify production improvements.
Experience working in production cloud environments and managing infrastructure with infrastructure as code (IaC).
Effective use of AI and coding agents for analysis, planning, implementation, and testing, with accountability for quality, security, and cost.
Excellent communication skills, including translating complex findings into clear technical and business decisions, listening to customer concerns, explaining risks and tradeoffs empathetically, and turning incomplete information into actionable next steps.
Ability to operate in a fast-paced startup with evolving priorities and incomplete requirements; identify valuable problems, make pragmatic decisions, test assumptions, and deliver useful increments while maintaining quality and customer trust.
Strong ownership and urgency: take initiative, meet commitments, adapt priorities thoughtfully, communicate status and risks early, and balance delivery speed with reliability, maintainability, and follow-through.
Willingness and ability to travel quarterly to the Chattanooga, Tennessee headquarters, with flexibility to travel up to monthly as needed.
Ability to work remotely in a distributed team with reliable high-speed internet, a professional workspace, and independent communication, collaboration, and execution.
Legally authorized to work in the United States at the time of hire.
Eligible and able to meet applicable customer requirements for work in GovCloud and CJIS-regulated environments, including required background screening, security training, and access restrictions.

Nice To Haves

Databricks experience, 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.
TypeScript, React/Next.js, and Python experience.
Knowledge of caching systems and strategies.
Experience building reusable optimization tooling or customer-facing data products.
Experience building high-quality, cost-effective data pipelines with tools such as dbt.
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.
Location in the Chattanooga, Nashville, Atlanta, or Knoxville area.

Responsibilities

Drive architecture, interfaces, and engineering standards for data optimization products; lead feature design, communicate tradeoffs, guide implementation, and plan for expansion across clouds and data systems.
Use and oversee AI agents to build, test, and mature platform features, backend services, customer integrations, analysis tools, and user-facing workflows.
Contribute production code and improve Stratusphere's data architecture, performance, and reliability.
Diagnose data-system 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.
Partner with FDEs, Product, and customer teams to identify optimization opportunities, establish baselines, test hypotheses, and implement changes.
Measure optimization results, document tradeoffs, and turn repeatable findings into roadmap priorities, features, and integrations.
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.
Identify recurring agent errors and missing context, and improve architecture, tooling, prompts, evaluation criteria, and workflows with Product and FDEs.
Stay current on agent-first development practices and contribute knowledge, processes, standards, and tooling that improve team velocity, quality, and maintainability.
Collaborate openly with Engineering, Product, FDEs, and customers; share context, surface assumptions and disagreements constructively, and ask questions early.
Own work end to end, communicate progress and risks proactively, follow change-control and customer experience standards, maintain documentation and work records, and close the loop on commitments.