Principal Analytics Engineer

Harness•Mountain View, CA

4d•$180,000 - $200,000

About The Position

Harness is the AI Software Delivery Platform company, led by technologist and entrepreneur Jyoti Bansal (founder of AppDynamics, acquired by Cisco for $3.7B). Harness has raised approximately $570M in funding and is valued at $5.5B, backed by leading investors including Goldman Sachs, Menlo Ventures, IVP, Unusual Ventures, Citi Ventures, and more. As AI accelerates code creation, the real bottleneck has shifted to everything after the code – testing, deployments, application security, reliability, compliance, and cost optimization. Harness brings AI and automation to this “outer loop,” helping teams ship software faster while maintaining security and governance throughout the entire software delivery lifecycle. Powered by Harness AI and the Software Delivery Knowledge Graph, the Harness Platform applies deep context and intelligent automation across the software delivery lifecycle with governance and policy-driven controls embedded throughout the platform. Over the past year, Harness powered over 185M deployments, 82M builds, 18T flag evaluations, 8M security scans, 9.1B optimized tests, 3T protected API calls, and helped manage $2.8B in cloud spend — enabling customers like United Airlines, Morningstar, and Choice Hotels to accelerate releases by up to 75%, reduce cloud costs by up to 60%, and achieve 10x DevOps efficiency. With a global team across 26 offices and 27 countries, Harness is shaping the future of AI software delivery — and we’re looking for exceptional talent to help us move even faster.

Requirements

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 Haves

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

Responsibilities

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