Principal Performance Engineer

At Bluebeam, we empower people to advance the way the world is built. We create smart software solutions that make construction sites more efficient, connected, and safe and improve the lives of design and construction professionals everywhere. 

The Principal Performance Automation Engineer sets the technical direction for load and performance engineering across the product portfolio, ensuring systems are scalable, efficient, and stable at production scale. This role owns the performance testing strategy, architecture, and tooling platform end to end. This includes defining benchmarks and service level objectives, leading deep investigations into complex bottlenecks, and translating results into engineering priorities.

What you'll do:

• Performance Strategy & Architecture – Define and own the long-term performance testing strategy, reference architecture, and roadmap for the load and performance team, aligning coverage with business and product risk.
  
• Technical Leadership & Mentorship – Lead the technical direction of the team, set coding and testing standards, review designs and code, and mentor performance engineers to raise the bar across the discipline.
  
• Performance Engineering at Scale – Architect and evolve frameworks, harnesses, and environments that simulate realistic production load, including large-scale data generation, distributed execution, and multi-tenant scenarios.
  
• Deep Diagnostics & Optimization – Lead root-cause investigations into complex bottlenecks, memory leaks, contention, and scalability limits, and drive optimizations to closure with development and DevOps teams.
  
• Benchmarks, SLOs & Governance – Establish performance benchmarks, service level objectives, and regression gates; embed automated performance validation into CI/CD pipelines so regressions are caught early and consistently.
  
• Cross-Functional & Executive Influence – Partner with engineering, DevOps, product, and support leadership to define performance goals, present findings to senior stakeholders, and influence architecture, capacity planning, and release decisions.
  
• Tooling & Platform Ownership – Evaluate, select, and own performance testing and observability tooling; build reusable platforms, dashboards, and automation that scale across teams.
• Documentation & Reporting – Produce authoritative performance test plans, models, and analyses, and deliver clear, data-driven reports and dashboards that support engineering and business decisions.
  

About you:

• 10+ years of experience in performance/automation engineering, software development, or DevOps, including 5+ years focused on load and performance engineering for large-scale or distributed systems.
• Bachelor’s or master’s degree in computer science, Engineering, or related field.
• Cloud Infrastructure – Deep hands-on experience with AWS services relevant to performance testing, scaling, and monitoring (e.g., EC2, CloudWatch, ELB, Auto Scaling, S3), including cost-aware test environment design.
  
• System Design & Architecture – Expert understanding of distributed systems, application workflows, data stores, caching, queuing, and infrastructure components; able to model and predict system behavior under load and influence architectural decisions.
  
• Programming (Required) – Coding is mandatory for this role. Strong software engineering skills are required to build and maintain k6 test suites, custom-built performance scripts and load generators, data generation, and result-processing tooling (e.g., in JS, TS, Python, C#), with emphasis on maintainable, reusable, reviewable code.
  
• Performance Testing Tools – Expert-level proficiency with industry-standard tools such as k6 for load, stress, and scalability testing.
  
• CI/CD Integration – Proven experience designing and owning performance test integration in pipelines using tools like GitHub Actions, or GitLab CI, including automated gating and trend tracking.
  
• Monitoring, Profiling & Analysis – Advanced skills with observability and profiling platforms such as Grafana, Prometheus, Dynatrace, and OpenSearch to correlate metrics, traces, and logs during investigations.
  
• Capacity Planning & Modeling – Ability to translate load test results into capacity, scalability, and sizing recommendations for production systems.
• Result Interpretation & Reporting – Expert at interpreting complex performance data and presenting findings through automated reports and dashboards using tools like Pandas, Grafana, and related visualization or data-processing frameworks.
  
• Technical Leadership – Demonstrated ability to mentor engineers, drive standards and best practices, and lead initiatives across teams without direct authority.
  
• Communication & Influence – Excellent written and verbal communication, strong investigative mindset, and ability to present technical findings and trade-offs to both engineering and executive audiences.
  
• AI Tooling – Practical, day-to-day use of AI assistants (e.g., GitHub Copilot, LLM-based tools) for script development, refactoring, test data creation, log and metric analysis, and report drafting, with sound judgment about verifying results and handling data appropriately.

Extra:

Cloud platform certifications (AWS, Google Cloud, Azure, etc.). Experience with profiling and APM tooling, chaos or resilience testing, database and query performance tuning, and performance engineering for large-scale SaaS or desktop applications.