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# Analytics Engineer

- **Company:** POSTSCRIPT LLC
- **Location:** United States (Remote available)
- **Experience:** Starter
- **Salary:** $80,000.0 - $100,000.0
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, Artificial Intelligence, Data Analysis, BigQuery, Information Engineering, Database Queries, Software Debugging, Tableau (Software), Snowflake, Vertica, Looker Analytics
- **Published:** July 25, 2026
- **Apply:** https://job-boards.greenhouse.io/postscript/jobs/8649676002

## About the Role

* 1-3 years of experience working in data, analytics, or business intelligence
 * Strong SQL skills - comfortable writing, reading, and debugging non-trivial queries (joins, aggregations, window functions, CTEs)
 * Familiarity with a cloud data warehouse (e.g., ClickHouse, Snowflake, BigQuery or similar)
 * Experience building data assets in dbt
 * Experience using AI to accelerate modeling, querying, and reporting
 * Experience building self-service tools in a BI platform (Omni, Looker, Hex, Tableau)
 * Clear written communicator that embraces documentation
 * Detail-oriented with a focus on data quality and integrity
 * Strong business acumen to understand data from many functional areas and anticipate stakeholder needs
 * High growth/early stage experience strongly preferred

## Description

The Analytics Engineering team is a high leverage function that plays a critical role in helping Postscript scale. Our mission is to build trusted and insightful data assets that improve decision making at all levels of the company. To accomplish our goals, we need versatile builders! You'll consult with stakeholders, build data pipelines, and enable self-service analytics. This is a high-impact role with lots of growth opportunities.

All positions at Postscript are fully remote within the United States.
Primary duties

 * Develop automated and resilient data transformation pipelines using software engineering best practices (e.g., version control, testing, code reviews)
 * Define processes for continuously monitoring data quality and build mechanisms to proactively resolve issues
 * Build the harness for agentic development including prompting, skills, and evals
 * Maintain detailed documentation and semantic layer for humans and AI agents to consume
 * Build self-service tools (e.g., reports, dashboards, integrations) to democratize data and improve decision making across all levels of the organization
 * Partner closely with Finance, GTM, and Product to define metrics, identify data needs, and build useful data assets
 * Collaborate with Data Engineering and Product to ensure upstream data sources are delivered with the right frequency, grain, and information to support business objectives


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