Sr. Software Engineer, Data Products

About the Role

Join a data organization that is becoming a software-engineering organization and is deliberately reinventing how engineering work is performed with AI.

This is a software engineering role in the data domain. You will build APIs, backend services, shared libraries, operational data stores, and data-processing systems that power internal and customer-facing capabilities. This is not a traditional ELT/ETL data engineer role.

The emphasis is on software engineering, not warehouse SQL or orchestration alone. Experience with BigQuery, Airflow, or similar tools is useful, but not sufficient by itself. We welcome software engineers with strong fundamentals and enough familiarity with data systems to learn the rest.

You will work on a small team within Zoro’s data organization, alongside product engineering and platform engineering. You will own systems, participate in design reviews, deploy and operate what you build, and take part in an on-call rotation.

AI-assisted engineering is a core part of how we work. We do not expect mastery. We do expect active experimentation, enthusiasm, and a willingness to keep improving.

You should be using AI to test designs, find underspecified requirements, break work into tasks and dependencies, explore unfamiliar code, and create artifacts that people and AI tools can use to review and execute work. You should also be experimenting with different tools and approaches and developing opinions about what works well for which kinds of tasks.

If you are excited about AI-assisted engineering, you will find a team learning quickly and sharing what works. If you do not want AI to be part of your regular engineering practice, this role is unlikely to be a fit.

What You Will Do

• Build and maintain production APIs, backend services, libraries, operational data stores, and data-processing applications.
• Participate in system design, design reviews, and technical tradeoff discussions.
• Design clear interfaces, service boundaries, schemas, and data contracts.
• Write modular, testable, observable, and maintainable software.
• Own systems through deployment, monitoring, incident response, maintenance, and retirement.
• Build reliable systems that handle retries, schema changes, duplicate events, partial failures, and unexpected inputs.
• Use AI throughout design, planning, implementation, testing, review, and codebase exploration.
• Validate AI-generated output and remain responsible for understanding and operating what you ship.
• Share useful AI workflows, experiments, and lessons with the team.
• Participate in on-call and improve systems based on production experience.

An Excellent Candidate Has

• Experience building backend services, internal platforms, developer tools, or distributed applications.
• Experience designing APIs and operational databases.
• Familiarity with asynchronous processing, queues, event-driven systems, or change-data capture.
• Experience handling idempotency, retries, concurrency, and backward compatibility.
• Familiarity with cloud infrastructure, containers, CI/CD, infrastructure as code, and observability.
• Experience with data pipelines, analytical systems, data modeling, or large-scale data movement.
• A habit of documenting AI experiments and developing repeatable workflows.
• Experience using AI to critique designs, find missing requirements, navigate codebases, generate tests, or evaluate implementation options.
• Practical opinions about where AI tools are useful, where they are unreliable, and how to improve their output.

How We Work

We value engineers who take ownership, prefer simple systems, treat operations as part of engineering, ask questions when designs are unclear, give thoughtful feedback, and balance speed with reliability.

We also expect engineers to use AI aggressively but not uncritically, and to share what they learn so the whole team improves.

Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Applied Mathematics, Engineering, or a related field, or equivalent practical experience.
• Experience: Minimum 5 years of experience in data engineering or software development, with a track record of designing and delivering production data systems.
• Table Stakes:
  • Experience building, shipping, and operating production software.
  • Strong programming skills in at least one general-purpose language.
  • Meaningful programming experience beyond SQL.
  • Understanding of modular design, APIs, testing, error handling, and maintainability.
  • Working knowledge of relational data concepts, including schemas, keys, transactions, and data integrity.
  • Experience with unit and integration testing and diagnosing production failures.
  • Willingness to own systems and participate in on-call.
  • Ability to contribute to design reviews and explain technical decisions clearly.
  • Active use of AI for more than code completion.
  • Sound judgment when evaluating AI-generated output.
  • A bachelor’s degree in a relevant field or equivalent practical experience.
• Soft Skills:
  • Technical Leadership: Influences team architecture and engineering practices. Initiates improvements without being asked.
  • Communication: Coordinates complex integrations across adjacent teams. Drafts design proposals and RFC documents clearly.
  • Mentorship: Guides junior engineers through data design patterns, system thinking, and AI-assisted development practices.
  • Product Thinking: Leads data products as first-class assets with consumer SLAs, versioning, and lifecycle ownership.
  • Judgment: Makes sound technical tradeoff decisions. Balances delivery velocity with long-term code health.