• Lead the design and development of quantitative data engineering models, including algorithms, data pipelines, and data processing systems, to support business requirements. • Develop and maintain data processing pipelines to ingest, clean, transform, and aggregate large volumes of data from various sources, ensuring data quality and reliability. • Design and implement algorithms for data analysis, machine learning, and statistical modeling, using techniques such as regression analysis, clustering, and predictive modeling. • Identify and implement optimizations to improve the performance and efficiency of data processing and modeling algorithms, considering factors like scalability and resource utilization. • Create visualizations of data and model outputs to communicate insights and findings to stakeholders. • Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data used in models and analyses. • Evaluate the performance of data engineering models using metrics and validation techniques, and iterate on models to improve their accuracy and effectiveness. • Collaborate with data scientists, analysts, and business stakeholders to understand requirements, develop models, and deliver insights that drive business decisions. • Document the design, implementation, and evaluation of data engineering models, including assumptions, methodologies, and results, to ensure reproducibility and transparency. • Stay updated with the latest trends, tools, and technologies in quantitative data engineering and data science, and continuously improve your skills and knowledge.

• Own day-to-day operations of the Burp Suite Enterprise DAST program: scan scheduling, agent and Linux infrastructure health, scan tuning, and result triage across multiple federal application environments. • Configure and troubleshoot authenticated scans against modern web applications and APIs, including recorded login sequences (via the official Burp recorder Chrome extension), session-handling rules, and macro-based re-authentication. • Diagnose and resolve Burp Enterprise scan failures end to end: consecutive audit-item failures, skipped insertion points, timeouts, session invalidation, and authentication state loss. • Extend Burp Suite Professional with custom extensions (Python/Java/Montoya API) to automate repetitive manual verification, custom authentication flows, and findings validation for the bug bounty program. • Design and implement authenticated scan workflows that survive multi-factor authentication, including SMS one-time passwords, TOTP tokens, hardware dongles, PIV and smart card client-certificate authentication, and SSO federation. • Administer the AppSec team’s own Linux infrastructure in AWS (currently EC2 with containerized Burp Enterprise components) and contribute to the migration to on-premise OpenShift. • Convert legacy Python and shell tooling left behind by previous engineers into Ansible roles and playbooks; manage YAML, Dockerfiles, and Kubernetes manifests as code. • Integrate AppSec tooling into GitHub Actions workflows alongside Dependabot SCA, including the appropriate use of workflow_dispatch versus workflow_call patterns and reusable workflows. • Provide secondary support to the broader AppSec toolset: Veracode SAST, Contrast IAST for interactive scanning and runtime security testing, GitHub Advanced Security workflows, and the HackerOne bug bounty program (validating reported findings with Burp Suite Professional).

• Manage, mentor, and develop a team of software engineers across all experience levels • Conduct regular 1:1s, provide timely and constructive feedback, and support each engineer’s professional growth and career development • Partner with recruiting to grow the team by attracting and hiring engineers aligned with the team’s AI-native technical direction • Set clear expectations, foster accountability, and build a culture of trust, collaboration, and continuous improvement • Address performance issues constructively and proactively • Own the team’s delivery commitments; ensure features, bug fixes, and technical investments are completed on time and meet quality standards • Participate in sprint planning, backlog refinement, and prioritization discussions with Product and stakeholders • Remove blockers, manage dependencies, and ensure the team has what it needs to execute effectively • Monitor and continuously improve team velocity, predictability, and delivery health within the agentic pod model • Maintain strong technical engagement: participate in architecture discussions, code reviews, and technical planning • Ensure the team adheres to engineering standards for C# / .NET, API design, database patterns, front-end development, and CI/CD using Azure DevOps • Identify and address technical debt; champion sustainable engineering practices • Drive adoption and effective use of AI-assisted development tools (GitHub Copilot, Claude, Azure OpenAI) across the team; ensure engineers are working in an agentic delivery rhythm where AI handles generation at scale and engineers own judgment, quality, and architectural decisions • Partner with other engineering leaders to define and uphold organizational AI development standards, prompt engineering practices, and AI output review requirements • Track and evaluate team-level engineering metrics, including sprint velocity and predictability, CI/CD pipeline health, production incidents, defect backlog trends, and system performance data to identify systemic issues and drive continuous improvement • Use data to communicate engineering health, technical decisions, tradeoffs, and statuses clearly to stakeholders and leadership • Contribute to engineering-wide initiatives including hiring practices, standards, tooling, and process improvement

• Design and implement full-stack features using C# / ASP.NET Core, Entity Framework Core, and SQL Server / Azure SQL for back-end services, and React or Angular for front-end applications • Write well-tested, maintainable code with unit and integration tests • Participate actively in code reviews, providing and incorporating constructive feedback • Contribute to Azure DevOps CI/CD pipelines and deployment workflows • Troubleshoot and resolve production issues in a timely manner • Use AI-assisted development tools (GitHub Copilot, Claude) with structured, context-rich prompts to accelerate coding, test coverage, and documentation • Critically evaluate all AI-generated output before committing — review for correctness, security risks, and alignment with team architecture and coding standards • Work within Libra’s agentic engineering model: AI agents handle code generation at scale; engineers own the judgment, quality bar, and architectural decisions on everything that ships • Collaborate with Product, QE, business stakeholders, and peers to deliver features that meet acceptance criteria and quality standards • Partner with offshore developers on the squad to scope tasks clearly, provide code review feedback, and maintain shared quality standards