Principal Analytics Engineer

Mosaic.tech; San Francisco (CA); On-site; USD 180,000 - 200,000; Full time

Job summary

Harness is seeking an experienced Analytics Engineer to own end-to-end data warehouse transformations, building and maintaining curated data models for product usage and go-to-market reporting.

You will enforce data governance, debug data issues across the stack, and collaborate with stakeholders to turn questions into well-scoped data models while reducing bottlenecks.

Qualifications

• 6+ years in analytics engineering or data engineering with 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.

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.

Skills

SQL

Data modeling

Python

BI tools

Cloud data warehouses

Tools

BigQuery

Snowflake

Redshift

dbt

SQLMesh

Job description

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.

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.

What You’ll Do

• 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

Required

• 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

Pay transparency

$180,000 — $200,000 USD

Harness in the news:

• Accelerating Our Mission to Bring AI to Everything After Code
• Goldman Sachs leads investment in software delivery startup Harness at $5.5 billion valuation
• How Harness runs 16 “startups within a startup” at scale | Jyoti Bansal
• Harness Research Shows AI Visibility Crisis Fueling Security Nightmare
• Harness has been named to the Inc. Power Partner list for software delivery success

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex or national origin.

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