• Internal implementation tools that help teams configure, manage, and deploy the product more efficiently.
• Client-facing data validation tools that allow customers to identify and resolve data issues before submitting data for implementation.
• Tools and services that reduce manual implementation effort and improve operational efficiency.
• Scalable applications and services capable of processing and working with very large datasets.
• Technical capabilities that support a high volume of customer implementations and go-live activities.
• Engineering solutions that can be adopted broadly across internal teams and customers.

Key Responsibilities

Architecture & Software Engineering

• Lead the design and implementation of complex tools, features, services, and technical initiatives across the SaaS platform.
• Design scalable, reliable, and maintainable applications using C#, .NET, ASP.NET MVC, and Blazor.
• Make architectural and technical decisions that support long-term scalability, performance, maintainability, and reliability.
• Design and develop APIs, services, integrations, and distributed application components.
• Apply sound software engineering principles, including SOLID principles, design patterns, separation of concerns, and maintainable architecture.
• Evaluate technical approaches and select solutions based on business requirements, system constraints, performance, and long-term maintainability.
• Translate clearly defined product and business requirements into robust technical designs and implementations.
• Partner with Product and implementation stakeholders to clarify requirements and identify the best technical approach without taking ownership of product management responsibilities.

Large-Scale Data & Performance

• Design and optimize applications that operate against very large datasets, including datasets exceeding 100 million records.
• Identify and resolve database, application, API, and architectural performance bottlenecks.
• Design efficient data access, processing, querying, and transformation strategies.
• Analyze system behavior under high data volumes and identify opportunities for significant performance improvements.
• Develop solutions that remain responsive, reliable, and scalable as data volumes and customer usage increase.
• Use profiling, monitoring, benchmarking, and other engineering techniques to validate performance improvements.
• Balance performance optimization with maintainability, reliability, and development velocity.

AI-Assisted Development

• Use Claude , the organization’s approved AI coding assistant, to accelerate development and engineering workflows.
• Apply AI to appropriate development activities, including coding, refactoring, debugging, test generation, documentation, and technical exploration.
• Create clear technical specifications, acceptance criteria, and implementation requirements that provide AI coding tools with sufficient context.
• Review and validate AI-generated code for correctness, security, performance, maintainability, and architectural fit.
• Use independent engineering judgment to determine when AI-generated solutions require modification, rejection, or deeper technical investigation.
• Help establish effective team practices for AI-assisted development while maintaining appropriate human review and accountability.
• Continuously evaluate how AI can improve engineering efficiency without compromising software quality.

Technical Leadership Mindset

• Act as a senior technical authority for complex engineering and architectural challenges.
• Provide technical guidance and mentorship to other engineers.
• Conduct code reviews and promote high standards for software quality and maintainability.
• Identify opportunities to improve engineering practices, tooling, architecture, and development efficiency.
• Participate in technical planning, design discussions, backlog refinement, and implementation planning.
• Communicate technical trade-offs and recommendations clearly to engineering, product, implementation, and business stakeholders.
• Take ownership of technical problems from initial discovery through implementation and production delivery.

Required Qualifications

• 10+ years of professional software development experience.
• Demonstrated experience operating at a senior, staff, principal, or architect-level capacity, owning complex technical decisions.
• Strong professional experience with C# and .NET.
• Strong experience developing modern web applications and services using ASP.NET MVC and/or Blazor.
• Proven experience designing and developing scalable SaaS applications or enterprise software.
• Demonstrated experience working with very large datasets, including applications or systems handling tens or hundreds of millions of records.
• Strong understanding of database performance, data access patterns, query optimization, application performance, and scalable system design.
• Experience designing and developing APIs and distributed services.
• Strong understanding of software architecture, design patterns, SOLID principles, and maintainable application design.
• Experience diagnosing complex performance, scalability, and reliability problems.
• Experience with Git and modern software development workflows.
• Experience using AI coding assistants, preferably Claude or comparable agentic coding tools.
• An active personal GitHub profile or public software repository is required. The repository should demonstrate genuine software development experience, technical curiosity, passion for engineering, or meaningful open-source/side-project work. Quality and substance are more important than the number of repositories.
• Strong written and verbal communication skills.
• Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.

Preferred Qualifications

• Experience building tools used by internal implementation, operations, or professional services teams.
• Experience building customer-facing data validation, configuration, onboarding, or implementation tools.
• Experience working with SaaS products that support complex customer implementations.
• Experience designing systems for high-volume data processing or data-intensive applications.
• Experience with cloud platforms, particularly Microsoft Azure.
• Experience with CI/CD and automated deployment practices.
• Experience with containerized or distributed application environments.
• Experience with observability, monitoring, profiling, and production performance analysis.
• Experience establishing or improving AI-assisted engineering workflows.
• Experience using Claude Code, GitHub Copilot, or other AI coding agents in professional development environments.
• Exposure to AI-enabled product features or technologies such as Azure OpenAI, RAG, or LLM-based applications.

What Success Looks Like

Success in this role goes beyond delivering individual features. The engineer will be expected to create tools that produce measurable improvements across the organization.

• High adoption: Internal implementation teams actively use the tools you build.
• Measurable efficiency gains: Tools reduce manual work, implementation time, or operational overhead.
• Better data quality: Client-facing validation tools help customers identify and resolve data issues earlier in the implementation process.
• Scalable performance: Applications continue to perform effectively as data volumes reach 100+ million records and customer usage grows.
• Customer impact: Tools contribute directly to smoother and faster customer implementations and go-lives.
• Technical excellence: Solutions are maintainable, well-architected, tested, and built to scale.
• Engineering leverage: AI-assisted development and improved engineering practices allow the team to deliver higher-quality solutions faster.

Architectural Thinker: You understand how individual technical decisions affect the broader system and can design solutions with scalability and long-term maintainability in mind.

Hands-On Engineer: You are comfortable working directly in the codebase and solving complex technical problems rather than operating exclusively at a conceptual level.

Scale-Oriented: You have experience building and optimizing systems that work with very large datasets and understand the engineering challenges that emerge at scale.

Tool Builder: You enjoy creating practical tools that make other people’s work faster, easier, and more reliable.

AI-Leveraged: You use AI coding tools as a force multiplier while maintaining complete ownership of the engineering outcome.

Technical Leader: You can make difficult technical decisions, explain tradeoffs, and raise the technical capabilities of the team.

Problem Solver: You enjoy taking ambiguous or complex engineering problems and turning them into practical, scalable solutions.

Engineering-Driven: You maintain a strong personal interest in software development and demonstrate that passion through your work, personal projects, open-source contributions, or GitHub activity.

Customer-Focused: You understand that the ultimate measure of a technical solution is the value it creates for customers and the teams using it.

Join Us and Enjoy These Perks!

Competitive Salary: Earn a USD salary that matches your skills and experience.

Paid Time Off: Generous PTO to support your work-life balance and recharge.

Health Insurance Subsidy: Financial assistance to support your healthcare coverage.

Annual Salary Reviews: Structured annual evaluations to recognize and reward

performance.

Paid Training & Certifications: Continuous support for professional growth and upskilling.

Ready to put your puzzle-solving skills to the test? Join our team today!

LinkedIn: Puzzle

Instagram: @ puzzle.tech

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