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Wrapbook is an AI-powered production finance platform trusted by Netflix, Paramount, and other major studios. The company is building systems that help finance teams manage payroll, spend, and accounting for feature films, TV, and commercials. Backed by Andreessen Horowitz, Bessemer Venture Partners, and WndrCo, Wrapbook has 350+ employees and is transforming how production finance teams work.
As Senior Analytics Engineer II, you'll own and evolve Wrapbook's analytics layer and data infrastructure. You'll build scalable data foundations that deliver fast, reliable, and actionable insights across the organization. Your work will span the full analytics stack: designing source systems and data models, creating governed metrics, enabling self-serve analytics, and implementing production monitoring.
Key responsibilities include leading analytics product development and self-serve tools so teams can access trusted data independently. You'll own analytics products end-to-end—from defining success metrics and designing schemas through testing, deployment, data quality, SLA management, and incident resolution. You'll build and maintain reliable ETL/ELT pipelines and canonical datasets supporting analytical workflows company-wide.
You'll structure the governed data layer with semantic models, canonical views, metric definitions, and data contracts, ensuring versioning and backward compatibility for downstream BI and AI consumers. A critical focus is making data usable by both people and AI agents through clear documentation and strong data quality controls. You'll mentor the Analytics team by setting standards for testing, dbt CI/CD, monitoring, alerting, automation, runbooks, and code review.
You'll partner with Engineering to understand source schemas, events, and system architecture, providing early input so data entering the warehouse is reliable and easy to model. You'll identify business questions, translate them into structured analytical problems, and develop data solutions that answer them.
Required: 4+ years in data or analytics engineering with hands-on experience building and operating production pipelines end-to-end. Strong SQL and Python skills, plus experience with ETL/orchestration tools (Airbyte, Fivetran, Dagster, dbt, Airflow). Experience with modern data warehouses (Snowflake, Databricks, Redshift) including query optimization. Strong data modeling skills and bias for action—shipping iterative models that deliver value. Experience building governed data layers, writing data quality checks (dbt tests, Great Expectations, Pydeequ), and using BI tools (Omni, Mode, Looker, Tableau). Track record of evaluating and adopting new data tools when they solve real problems.
