Senior Analytics Engineer

Chainguard is the trusted source for open source. By delivering hardened, secure, and production-ready builds of all the open source software engineers and AI agents rely on, Chainguard helps organizations build faster, stay compliant, and eliminate risk. Our customers include Fortune 500 enterprises and global industry leaders, including Anduril, Canva, Fortinet, Hewlett Packard Enterprise, OpenAI, Snap Inc., and Snowflake. Chainguard is venture-backed by leading investors, including Amplify, IVP, Kleiner Perkins, Lightspeed Venture Partners, Mantis VC, Redpoint Ventures, Sequoia Capital, and Spark Capital.

The role, in a nutshell:

We are looking for a Senior Analytics Engineer to join our Analytics team and help build the data foundation that powers decisions across the company.

This is a high-impact senior individual-contributor role for someone who can own and evolve data infrastructure, analytical models, and self-service data products while partnering closely with stakeholders across Finance, Go-to-Market, Product, Engineering, Customer Success, and other business functions.

You will join a small, growing team with a modern data stack already in place and meaningful opportunity to shape how we work. You will bring strong technical judgment, business curiosity, and the ability to translate between technical systems and real business needs. As a senior team member, you will independently lead ambiguous initiatives, help establish durable standards, mentor teammates, and enable stakeholders to make better decisions with trusted data.

What you'll do:

Own and evolve core components of our analytics platform, including data models, pipelines, metric definitions, semantic layers, and self-service reporting experiences.

Design, build, and maintain scalable, reliable data models and transformations using dbt and modern analytics-engineering practices.

Develop trusted sources of truth that support decision-making across the company, balancing immediate business needs with long-term scalability and maintainability.

Partner directly with stakeholders to gather requirements, understand business problems, translate needs into technical solutions, and communicate tradeoffs clearly.

Build and improve dashboards, analytical tools, and enablement materials that help business partners understand, explore, and confidently use data independently.

Own the full lifecycle of data products: discovery, design, implementation, testing, documentation, deployment, monitoring, governance, and iteration.

Improve the reliability, observability, performance, and data quality of ELT workflows and integrations across our modern data stack.

Establish and raise standards for data modeling, testing, documentation, lineage, governance, access controls, and metric consistency.

Use AI thoughtfully to improve analytics workflows, enable new forms of analysis, and help the business get more value from its data.

Independently triage technical and data-quality issues, identify root causes, and drive durable resolutions without requiring day-to-day direction.

Mentor and support more junior analytics teammates through technical guidance, code review, knowledge sharing, and reusable patterns.

Help shape the technical roadmap for a growing Analytics function by identifying opportunities for automation, process improvement, and scalable platform investments.

What success looks like:

Within your first six months, you will have built strong working knowledge of the company’s key business domains, data systems, and stakeholder needs. You will be able to independently own significant parts of the technical stack, triage and resolve issues, and lead data initiatives from problem definition through adoption.

Over time, you will become a trusted technical and strategic partner across the business, making it easier for teams to access reliable data and allowing the broader Analytics team to operate with greater speed, consistency, and impact.

What we're looking for:

5+ years of experience in analytics engineering, data engineering, business intelligence engineering, or a closely related field; equivalent depth of experience will also be considered.

Demonstrated success leading complex, cross-functional data initiatives from ambiguous problem definition through delivery, adoption, and measurable business impact.