Senior Software Engineer
As a Senior Software Engineer, you will design, build, and operate software that solves complex business problems through thoughtful use of data, AI, and experimentation. You will turn ambiguous needs into reliable applications, services, and platforms that support teams across Sales, Marketing, Finance, HR, and related functions.
You will have opportunities to explore emerging technologies, develop prototypes, and bring promising AI and data science capabilities into production. You will partner with engineers, data scientists, and business stakeholders to evaluate technical approaches, define success criteria, and deliver maintainable solutions with measurable impact.
You will independently lead substantial technical projects within your team’s scope, contribute to system design and engineering standards, and provide guidance to other engineers. Success requires sound engineering judgment, comfort with uncertainty, and the ability to balance experimentation with dependable delivery.
What You’ll Do:
Own software delivery from problem definition through production. Translate business needs into technical requirements, designs, and implementation plans; build, deploy, and maintain solutions; and take responsibility for their ongoing performance and reliability.
Design maintainable systems. Build applications, APIs, services, and data processing components with clear interfaces, appropriate abstractions, and reusable code.
Apply AI and machine learning to practical problems. Partner with data scientists and other specialists to integrate models, AI services, and analytical capabilities into software products and business workflows.
Explore and validate new approaches. Develop prototypes and proofs of concept, test technical feasibility, and evaluate emerging tools and techniques. Use evidence to recommend whether to advance, revise, or discontinue an approach.
Build reliable data integrations and pipelines. Work with structured and unstructured data, establish data quality checks, and develop components that support applications, analytics, and model inference.
Define and measure success. Establish metrics for software reliability, performance, AI output quality, and business impact. Use experiments and statistical analysis where appropriate to evaluate outcomes and guide improvements.
Make sound technical tradeoffs. Assess architecture options, implementation complexity, operating costs, and build-versus-buy decisions. Communicate recommendations clearly to technical and non-technical partners.
Maintain a high engineering quality bar. Write readable, tested, and documented code; participate in code and design reviews; and use version control, automated testing, and CI/CD practices.
Operate and improve production systems. Diagnose failures, address performance bottlenecks, and improve monitoring and recovery processes. Incorporate appropriate safeguards for sensitive data and AI-enabled workflows.
Strengthen the team’s technical capabilities. Contribute to shared tooling and engineering practices, share findings from experiments, and mentor colleagues through design discussions, code reviews, and collaborative problem-solving.
The information in this job description represents a summary of the role and is not intended to be a comprehensive list of job duties. Responsibilities and duties of the position may change without notice at the Company’s discretion.
What You Bring:
Bachelor’s degree in computer science, engineering, or a related technical field, or equivalent practical experience.
5+ years of professional software engineering experience, including substantial ownership of software deployed and maintained in production.
Demonstrated ability to independently deliver complex technical projects, navigate ambiguous requirements, and make thoughtful design and implementation decisions.
Strong proficiency in Python and SQL, with experience building maintainable software beyond exploratory scripts or notebooks.
Solid understanding of software design, APIs, data structures, automated testing, version control, and CI/CD.
Experience building and operating cloud-based applications, services, or data-intensive systems, including troubleshooting production issues and improving reliability.
Experience working with data pipelines, databases, and integrations, with attention to data quality, performance, and access controls.
Working knowledge of applied AI, machine learning, or statistical methods, including how to evaluate their suitability, limitations, and performance in a software solution.
Ability to explain technical decisions and tradeoffs, collaborate across disciplines, and provide constructive guidance to other engineers.
Preferred Qualifications
Master’s degree in computer science, engineering, or a related technical field, or equivalent practical experience.
Familiarity with MLOps practices - model versioning, monitoring, drift detection, CI/CD for ML.
Experience designing and maintaining dashboards for operational or executive audiences.
Experience presenting analyses to senior technical and non-technical audiences.
Exposure to Sales, Marketing, Finance, or HR analytics domains.
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