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# Principal Analytics Engineer

- **Company:** Harness Inc.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $180,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, BigQuery, Information Engineering, Data Security, Software Debugging, Python (Programming Language), Metadata Repositories, Operational Databases, Mixpanel, Standard Sql, Salesforce.Com, Tableau (Software), Workflow Management Systems, Data Processing, Scripting, Large Language Models, Snowflake, Looker Analytics, Amazon Redshift
- **Published:** September 4, 2026
- **Apply:** https://www.dice.com/job-detail/f44f2f16-f1d9-4905-a99f-77ad116ce1f0

## About the Role

* 6+ years in analytics engineering, data engineering, or similar, with real ownership of production data models
 * Strong SQL and a track record shipping analytical models others depend on
 * Hands-on with a modern cloud warehouse (BigQuery, Snowflake, Redshift, or similar)
 * Experience with a transformation framework (dbt, SQLMesh, or equivalent)
 * Scripting (Python or similar) for ingestion, validation, or automation
 * Experience with a BI tool (Tableau, Looker, Metabase, or similar)
 * Demonstrated judgment on data access and sensitive data handling

Nice to Have

 * Experience with data-backed AI applications - RAG pipelines, embeddings, LLM-powered internal tools (active area of investment for us)
 * Product telemetry / event-stream data (Segment, Amplitude, Mixpanel)
 * CRM/CS/GTM data sources (Salesforce, Gainsight, Outreach)
 * Data catalogs, lineage tools, governance frameworks
 * Workflow orchestration (Airflow or similar)
 * PLG, adoption, entitlement, or customer health reporting

## Description

* Own warehouse transformations end to end - from raw ingestion through documented, stable tables that downstream teams and tools depend on
 * Build and maintain curated data models for product usage, customer adoption, account health, and go-to-market reporting
 * Set and enforce data practices: governance, access patterns, model ownership, freshness standards
 * Debug data issues across the full stack - source systems, pipelines, warehouse models, BI dashboards - and drive root-cause fixes, not just patches
 * Improve documentation and lineage so teams can self-serve without tribal knowledge
 * Partner directly with stakeholders across the business to turn ambiguous questions into well-scoped data models
 * Unblock teams on access, metric definitions, and data discoverability as part of a broader effort to reduce reliance on you as a bottleneck


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