Principal Engineer - Data

About the role

The role defines how Buildkite's deep build and test history becomes the foundation for agentic data access. It sets the architecture so that both humans and autonomous agents can discover, trust, and act on data safely and at speed.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Architect the semantic layer and query engine so agents and humans can reliably find and act on data.

Design data experiences for both internal teams and external agents, keeping self-serve accessible to non-technical users.

Own performance and correctness tradeoffs, including the migration path from Athena to ClickHouse.

Requirements

Have owned data architecture in production and made the architectural calls yourself, not merely advised on them.

Bring strong opinions on modern data platforms and the end-to-end stack, including semantic or headless-BI layers, query engines, analytical stores, transformation and modeling, ingestion and streaming.

Understand how agentic AI consumes data and what a semantic layer must provide to agents and self-serve users.

Be comfortable working with a monolithic Ruby product while focusing on data infrastructure, and be fluent with Kafka or Flink when relevant.

Design with privacy and compliance considerations in mind from the start.

Practical notes

This is a principal-level, architecture-focused role with no people-management expectations for the foreseeable future.

The role expects high autonomy, remote async work, and ownership of decisions that carry both technical and product impact.

You must be located in the APJ region, and Buildkite currently cannot offer sponsorship.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data platforms for agentic workflows are an emerging discipline with few established patterns.

The role works on tools such as semantic layers, query engines, and analytical stores to serve both human and machine consumers.

Ownership and clear trade-offs define success more than process or hierarchy.

Remote work is asynchronous by design, with deliberate overlap to maintain alignment.

The scale involves systems that serve billions of daily users across strong engineering organizations.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.