Principal Analytics Engineer at Harness

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

03 Oct, 26

Salary

0.0

Posted On

05 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Analytics Engineering, Data Engineering, Cloud Data Warehousing, dbt, SQLMesh, Data Modeling, ETL Pipelines, BI Dashboards, Data Governance, Data Lineage, Product Telemetry

Industry

Software Development

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. 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. Position Summary We're looking for a Principal Analytics Engineer to join our data platform team. You'll sit at the intersection of raw data and the people who rely on it, building and maintaining the models, pipelines, and reporting surfaces that power decisions across the company. This is a hands-on technical role with real stakeholder exposure. The right person is comfortable tracing a data issue from source system to dashboard, knows how to turn a vague business question into a well-scoped data model, and cares about making data trustworthy and easy to use. What You'll Do Build and maintain curated data models for product usage, customer adoption, account health, and go-to-market reporting. Own warehouse transformations end to end, from raw ingestion through to the stable, documented tables that downstream tools and teams depend on. Support ingestion from a variety of sources: SaaS platforms, product telemetry, APIs, spreadsheets, and operational databases. Debug data issues across the full stack, source systems, pipelines, warehouse models, and BI dashboards. Partner with stakeholders across product, sales, customer success, marketing, finance, and engineering to understand and deliver on data needs. Triage and resolve data support requests, including access, metric definitions, broken dashboards, and data discoverability questions. Improve documentation, data lineage, and discoverability so teams can self-serve without relying on tribal knowledge. Help establish and reinforce good data practices, governance, access patterns, model ownership, and freshness standards. What We're Looking For 6+ years in analytics engineering, data engineering, or a closely related role. Strong SQL and experience shipping production-grade analytical models. Hands-on experience with a modern cloud data warehouse (BigQuery, Snowflake, Redshift, or similar). Experience with a transformation framework like dbt, SQLMesh, or equivalent. 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. At Harness, we care about your privacy and are committed to protecting your personal data. For additional information on this topic, you can visit our privacy Portal: https://harness-privacy.relyance.ai/ Note on Fraudulent Recruiting/Offers We have become aware that there may be fraudulent recruiting attempts being made by people posing as representatives of Harness. These scams may involve fake job postings, unsolicited emails, or messages claiming to be from our recruiters or hiring managers. Please note, we do not ask for sensitive or financial information via chat, text, or social media, and any email communications will come from the domain @harness.io. Additionally, Harness will never ask for any payment, fee to be paid, or purchases to be made by a job applicant. All applicants are encouraged to apply directly to our open jobs via our website. Interviews are generally conducted via Zoom video conference unless the candidate requests other accommodations. If you believe that you have been the target of an interview/offer scam by someone posing as a representative of Harness, please do not provide any personal or financial information and contact us immediately at security@harness.io. You can also find additional information about this type of scam and report any fraudulent employment offers via the Federal Trade Commission’s website (https://consumer.ftc.gov/articles/job-scams), or you can contact your local law enforcement agency.

Responsibilities

Build and maintain curated data models and warehouse transformations to power business decisions across the company. Partner with cross-functional stakeholders to deliver data needs and improve data discoverability and governance.