Sr. Software Development Engineer

About the Team

Responsibilities

• Provide technical leadership and strategic direction for the team.
• Lead the design, implementation, documentation, deployment, and adoption of scalable, resilient distributed systems and microservices.
• Design well-factored APIs and event-driven integrations between services, with attention to performance, fault tolerance, and backward compatibility.
• Ensure data integrity, security, and observability across services, and secure handling of sensitive financial data.
• Break down quarterly deliverables into well-organized stories, weaving in reliability, scalability, security, and performance.
• Mentor less-experienced engineers through code reviews, design reviews, and guidance.
• Work with product and clients to uncover internal and external user needs.
• Track and communicate team metrics (delivery, quality, operational health).
• Get proactively involved in urgent issues and propose effective solutions; own on-call and operational excellence.
• Take on unowned work that improves team velocity and quality.
• Stay current with industry trends and emerging technologies, and evaluate their fit.
• Perform other reasonable duties as required by management.

Required Qualifications

• Bachelor's or master's degree in engineering, computer science, or a related field.
• 7+ years of enterprise software development in Java or another backend language (e.g., Python, Go).
• Experience designing and implementing distributed systems using microservices and event-driven architectures.
• Professional experience running production systems on a public cloud (AWS, Azure, or GCP), including containers, CI/CD, and observability.
• Strong data modeling skills and experience with relational and NoSQL data stores.
• Experience with APIs (REST/gRPC), messaging/streaming (Kafka, SQS, etc.), and designing for scalability and resilience.
• Strong analytical and problem-solving skills, with excellent attention to detail.
• Excellent written and verbal communication, and strong documentation habits.
• Commitment to continuous learning, client satisfaction, and teamwork in a SaaS environment.

Preferred Qualifications

• Exposure to AI/ML or LLM-based features (RAG, embeddings, prompt engineering) or interest in applying AI to engineering workflows.
• Familiarity with financial services, investment accounting, or fixed-income data.
• Experience with data governance, privacy, and regulatory/compliance requirements.
• Infrastructure as code (Terraform, CloudFormation) and Kubernetes experience.
• Track record of technical mentorship and leading cross-team initiatives.