Senior Software Engineer

Design and develop technical solutions as part of an agile team Work in a dynamic and exciting agile environment with Scrum Masters, Product Managers, and team members to develop creative data-driven solutions that meet business and technical initiatives Work directly with data scientists to provision data for model development and AI workflows Interact with internal stakeholders and the core development team to refine the product backlog Automate and execute all levels of testing (unit, integration, and regression) Design and develop solutions to support ingestion, curation, and provisioning of complex enterprise data to achieve analytics, reporting, and data science Support custom applications through their entire lifecycle Proactively avoid and eliminate technical debt Champion engineering excellence Foster a learning/growth mindset and model it for others Programming Languages: Python, TypeScript/Node.js Frontend: React Data: SQL and relational database concepts, Postgres / pgvector Service Architecture: Microservices, REST APIs, JSON, Postman CI/CD: GitHub Actions (preferable) or equivalent experience such as Bamboo, Jenkins, or Azure DevOps AWS: CDK, Glue, Lambdas, S3, Step Functions, DynamoDB, ECS, Fargate, SQS, API Gateway, Agent Core Observability: Data Dog, MLflow 3 GenAI: Experience with LLM orchestration, search systems, and context engineering Experience with workflow observability, including prompt and model tracking tools (MLflow, Lang Smith, or equivalent) A minimum of five years of software engineering experience A Bachelor's or Master's degree in a technical or business discipline, or equivalent experience Generally 4-6+ years of professional experience Strong oral and written communication skills; presentation skills Proficient in consensus building and facilitation Ability to learn and adopt new technologies Understanding of AWS architectures and technical standards Proficiency in multiple programming languages and tools Understanding of agile data engineering concepts and processes Consultative skills, including the ability to understand and apply customer requirements, drawing out unforeseen implications and making recommendations for design, the ability to define design reasoning, and understanding potential impacts of design requirements Collaboration, prioritization, and adaptability skills required AI Fluency: Actively uses AI tools to improve the speed, quality, and scale of work; can point to concrete workflows where AI adds value; critically evaluates and validates AI-generated output; and applies AI responsibly, protecting confidential and customer data.