Analytics Engineer at Chalk in SF

Job description

• Application
• We're hiring an Analytics Engineer to own the data layer that powers decision-making across Chalk. When a customer signs up, a lot happens behind the scenes: they get set up in our systems, we start recording their usage, and the right rates and terms get attached. That data flows all the way through to the metrics that leadership, Finance, and GTM use to run the business. You'll own the pipelines, data models, and analytics that make every step of that path trustworthy.
• This is a hands-on role at the intersection of data engineering and analytics. You'll rebuild pipelines that have grown up organically as we've scaled, design the data models that keep customer, usage, and billing data clean and reliable, and turn manual back-office processes into automated, auditable systems.
• We're in the office 5 days a week. When unavoidable conflicts come up, we’re flexible. This is not a hybrid role.

Responsibilities

• Own and evolve Chalk’s core data layer, creating trusted models and definitions used across the business.
• Help build and maintain reliable pipelines that bring product, customer, commercial, and financial data into the warehouse.
• Transform raw data into clean, documented, reusable datasets for reporting, analysis, and operational workflows.
• Establish consistent definitions for key business metrics across usage, customers, revenue, and product adoption.
• Improve data quality through testing, monitoring, reconciliation, and clear ownership.
• Partner with Finance, Product, Engineering, Sales, and Operations to translate business questions into durable data solutions.
• Build self-service tools and datasets that make it easier for teams to answer questions independently.
• Improve the reliability, performance, and maintainability of Chalk’s analytics infrastructure.
• Help define best practices for data modeling, governance, documentation, and access.
• Identify gaps in the data stack and help shape its long-term architecture and roadmap.

Requirements

• 4+ years of experience in analytics engineering, data engineering, or a similar hybrid data role.
• Experience building data models for billing, usage metering, or financial reporting - you understand why correctness, idempotency, and auditability matter when the output is an invoice.
• Strong Python skills - you're comfortable writing and maintaining production pipeline code, not just notebooks.
• Expert-level SQL and deep, hands-on experience with a cloud data warehouse (we use BigQuery).
• Fluency with modern analytics tooling - dbt or similar transformation frameworks, and BI/notebook tools.
• Strong data intuition: you catch the anomaly in the chart before anyone else does, and you don't ship a number you can't explain.
• Comfort operating with ambiguity and ownership - you'll be the first person dedicated to this domain, and you'll define how it works.
• Clear written communication. You can explain a pipeline design or a revenue variance to a finance leader and an engineer in the same doc.

Skills

Required

• Python
• SQL
• dbt
• BigQuery
• BI tools
• Notebook tools