Senior Software Engineering Lead

Caring. Connecting. Growing together.

Primary Responsibilities

• Responsible for large and more complex components, while influencing overall Data architecture and patterns
• Implements component design in line with pre-defined team architectural patterns
• Creates design documents, for larger component work to gain alignment
• Delivers designs aligning to appropriate componentized architectures and integration patterns
• Can work autonomously
• Influences the team designs and solutions. Mentors software engineers through code reviews, and hands-on design sessions
• Code reviews are appropriately broken down in to reviewable chunks
• Contributes meaningfully to code reviews of teams work, providing collaborative guidance in areas of strength
• Continues to receive guidance in own code reviews primarily around solution refinement, rather than overall direction. Able to explain implementation decisions and push back appropriately
• Learns from feedback and applies to future deliverables
• Solves more complex problems
• API's / Data Structures/ Data Models/Algorithms/ Application Sequences are thoughtfully designed
• Solutions are well integrated, testable, maintainable and performant
• Appropriately leverages existing solutions and adapts for reuse
• Delivers solutions with the appropriate toolset (languages, algorithms, patterns and frameworks) for the constraints and conditions of the business, team and product
• Leaves code better than when they found it. #CampsiteRule
• Choose refactor opportunities to drive drive down tech debt, in alignment with sprint and program goals
• Incorporates automation in testing, build, and deployment processes to drive team efficiencies
• Introduces automation to replace repeated manual processes demonstrating measurable improvement
• Works with product management to ensure stories are well worded, have appropriate acceptance criteria and are sized appropriately
• Drives completion of deliverables that have dependencies on other people or teams ensuring an on time finish
• Unblocks the team when they're blocked Limits work in progress by shipping features / deploying product often
• Decomposes features into manageable stories/tasks
• Considers the user experience, business impact and solution objective when managing development and design of solutions
• Understands business/financial impact of stability and resiliency
• Understands the financial implications of software (cost and benefits for both engineering and operations)
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
  

Required Qualifications

• Here is the Core Technical Skills
• Leadership & Delivery
• Agile methodologies (Scrum, Kanban, SAFe)
• Stakeholder management and cross-functional collaboration
• Enterprise application lifecycle ownership
• Team mentoring, performance management, and delivery accountability
• Cloud & Infrastructure
• AWS
• Infrastructure as Code (Terraform, CloudFormation)
• Kubernetes, Docker, CI/CD (GitHub Actions, Jenkins)
• Frontend Development:
• React.js
• Responsive UI design and performance optimization
• Backend & API Development:
• React.js, TypeScript
• Performance tuning
• Data Engineering & Analytics
• ETL pipelines, Data Lakes, AWS Glue, Athena
• SQL, NoSQL (DynamoDB, MongoDB)
• Integration with BI tools (Power BI)
• Generative AI & LLMs
• Hands-on experience with LLM platforms: OpenAI (GPT), Anthropic (Claude), Google (Gemini)
• Experience in Vibe Coding & Rapid Prototyping
• Prompt engineering and optimization for task-specific outcomes (e.g., summarization, Q&A, code generation)
• Fine-tuning and customizing LLMs using frameworks like Transformers, LangChain
• Building LLM-powered applications: copilots, chatbots, semantic search
• Integration with vector databases for retrieval-augmented generation (RAG)
• Responsible AI practices: bias detection, explainability, safety guardrails