Data Warehousing Engineer Jobs in California (NOW HIRING)

• Own the architecture of our data platform, spanning ingestion (Hevo, Estuary), our Google BigQuery warehouse, and the dbt transformation layer — designing systems that are scalable, reliable, secure, and built to last.
• Set the technical bar as a senior IC, leading through design reviews, code review, and mentorship — raising the quality, rigor, and engineering discipline of the team without a formal management role.
• Build and harden data ingestion at scale, designing batch and CDC pipelines across a diverse set of sources — operational databases, SaaS applications, and ERP systems — and driving vendor and tooling decisions with a clear point of view.
• Develop end-to-end with AI coding agents. Our teams build with Claude Code and similar tools, and you'll help define the practices, guardrails, and standards that make AI-assisted engineering fast, safe, and high-quality.
• Invest in reliability and observability, establishing testing, monitoring, alerting, and incident-response practices so data lands on time, is trusted, and failures are caught before stakeholders notice.
• Strengthen governance and controls, implementing the access models, auditability, and operational rigor expected of a company operating at scale and under growing scrutiny.
• Lay the foundations for agentic analytics, building the well-modeled, well-governed platform that agents and MCP connections rely on to deliver trustworthy answers from data.
• Partner with analytics engineers, analysts, and stakeholders to understand data needs, unblock high-value use cases, and translate business requirements into durable platform capabilities.
• Engineer tooling and services beyond the warehouse - building the APIs, automated jobs, and internal tools in Python that make the platform run smoothly and put data in more people's hands than SQL alone can reach.
• Drive the evolution of our warehouse architecture - leading hands-on POCs on lakehouse patterns, open table formats, and alternative query engines, and deciding what's worth adopting next.

• A seasoned data engineer with 8+ years building data platforms and pipelines in production, with a track record of technical leadership at the staff or senior level.
• An expert in the modern data stack. You have deep, hands-on experience with cloud data warehousing (BigQuery or equivalent), dbt, and modern ingestion tooling — and strong opinions, loosely held, about when to buy versus build.
• A systems thinker. You design for scale, failure, and change — and you can articulate the trade-offs between architectural options clearly, in writing and in person.
• Fluent in AI-assisted development. You have real experience developing with Claude Code or a similar coding agent, and you treat it as a core part of how modern engineering gets done rather than a novelty.
• Reliability-obsessed. You believe pipelines should be tested, observable, and boring — and you've built the tooling and practices to make that true on teams you've worked with.
• Governance-minded without being risk-averse. You know how to design access controls, auditability, and data-quality safeguards that earn trust in the numbers without slowing the team down.
• A force multiplier. You raise the bar for those around you through mentorship, review, and example, and you're energized by making other engineers better.
• Technically fluent and a clear communicator. You're expert in SQL and Python, deeply comfortable with version control, CI/CD, and testing workflows, and able to explain technical decisions to audiences from individual contributors to executive leadership.
• Cloud-infrastructure fluent. You work comfortably beyond the data warehouse, managing serverless compute, storage, and secrets on a major cloud platform like GCP or AWS.
• A forward-thinking architect. You stay ahead of where the data stack is heading, with well-reasoned views on lakehouse patterns and query engines that you test through hands-on work.

Demonstrates expertise in building scalable data platforms and pipelines, with a strong focus on reliability, governance, and AI-assisted development. Proficient in cloud data warehousing, data ingestion tooling, and architectural design for modern data stacks.

• Data Platform Architecture
• Cloud Data Warehousing (BigQuery)
• AI-Assisted Development (Claude Code)
• Data Ingestion Tooling (Hevo, Estuary)
• SQL and Python Proficiency

• Data Pipeline Development
• Cloud Infrastructure Management
• CI/CD Workflows
• Testing and Monitoring Practices
• Access Control Design
• Data Quality Safeguards
• Batch and CDC Pipeline Design
• Version Control
• Lakehouse Patterns
• Automated Job Engineering

• Technical Leadership
• Mentorship
• Clear Communication
• Systems Thinking
• Collaboration

• Data Governance
• Observability
• Operational Rigor
• Data Quality
• Analytics Engineering

• Google BigQuery
• Dbt
• Hevo
• Estuary
• Claude Code
• GCP
• AWS