Why This Job is Featured on The SaaS Jobs
This Analytics Engineer role stands out in SaaS because it applies modern analytics engineering practices to internal “business-as-a-product” use cases: real estate, workplace operations, and finance. Within software-led organizations, these domains increasingly rely on the same rigor as customer-facing products, including governed metrics, reliable pipelines, and decision-grade reporting. The remit also touches real-time and sensor-driven data, a less common but growing pattern as SaaS companies instrument physical operations alongside digital workflows.
From a SaaS career perspective, the role builds durable strengths in metric design, stakeholder-aligned modeling, and analytics product delivery. Working across multiple systems and operational datasets mirrors the integration challenges seen in many SaaS environments, where data consistency and trust determine adoption. The emphasis on quality checks, monitoring, and canonical definitions supports a portfolio of work that translates across analytics engineering teams, data platform groups, and BI enablement functions.
This position is best suited to professionals who enjoy turning ambiguous operational questions into well-defined datasets and Tableau assets, and who prefer measurable delivery over exploratory analysis alone. It will fit someone comfortable partnering with non-data teams and iterating on shared definitions, particularly where reliability and timeliness matter. Candidates interested in bridging batch analytics with near real-time applications should find the scope aligned with that trajectory.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Team
The Real Estate & Workplace (REW) Data & Technology team builds the data foundation that powers how OpenAI plans, operates, and scales its global real estate and workplace footprint. We integrate data across people, seating, presence, meeting rooms, sensors, leases, and finance systems to create a trusted source of truth that enables efficient, high‑impact decisions about how we design and operate our offices.
Our data platform also serves as the backbone for agent‑based automation and real‑time applications, giving you the opportunity to contribute to cutting‑edge AI innovation that is transforming how workplaces and real‑estate operations run across OpenAI.
About the Role
As an Analytics Engineer on the REW Data & Technology team, you will build high‑quality data models, pipelines, and Tableau assets that enable OpenAI to make data‑driven decisions about offices, workplace operations, and employee experience. You will work closely with Workplace Operations, Finance, and Technology teams to translate real‑world operational problems into reliable, scalable analytics products.
This role is focused on strong execution and delivery: shipping trusted datasets, building production dashboards, and contributing to a growing analytics platform that supports both human and agent‑driven workflows.
In this role you will:
Build and maintain business‑facing data models in Databricks and SQL that power REW reporting and planning.
Develop and manage data pipelines for Tableau dashboards used by site leaders, operators, and executives.
Define and maintain canonical REW metrics and documentation to ensure consistent decision-making across teams.
Integrate data from HR systems, OfficeSpace, badge and access systems, meeting‑room bookings, and real‑time presence and occupancy sensors.
Implement data quality checks, tests, and monitoring to ensure the reliability of REW metrics.
Build real‑time data pipelines that support agent‑based operational applications.
Partner with product managers and engineers to evolve and scale the REW data platform.
You Might Thrive in This Role If You Have
6+ years of experience in analytics engineering, data engineering, or analytics.
Strong SQL skills and experience working with cloud data platforms.
Comfort working with messy, operational, and real‑world datasets such as environmental sensor data, IoT data, or other event‑based data sources.
Experience with tools such as dbt, Airflow, or Spark, and real‑time systems such as Kafka, Kinesis, Delta Live Tables, or Spark Structured Streaming.
The ability to thrive in a fast‑moving, ambiguous environment where priorities can shift quickly.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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