# Data Engineering Manager, Growth & Revenue
**Company:** [OpenAI](https://scaleengineer.com/companies/openai)
Lead the Growth & Revenue data engineering organization at OpenAI, responsible for building and scaling trustworthy data systems that power growth accounting, subscription management, billing, payments, and revenue measurement across all product surfaces. This role requires a strong engineering leader with deep expertise in data systems architecture, team scaling, and cross-functional partnership to deliver durable data products connecting product behavior to financial truth. Partner with Data Science, Finance, and Product teams to establish metric definitions, data quality standards, and self-serve analytics that drive fast, consistent decision-making across the organization.
**Role:** Engineering Manager
**Seniority:** Manager
**Locations:** San Francisco
**Salary:** 459000–536000 USD
[Apply](https://jobs.ashbyhq.com/openai/acbf80cd-20a7-47fe-aaf9-3e9224d1570c)
Canonical: https://scaleengineer.com/jobs/openai/data-engineering-manager-growth-revenue
---
## Responsibilities

- Build and Scale High-Performing Data Engineering Team: Recruit, develop, and manage a diverse, inclusive team of data engineers across Growth and Revenue data subject areas. Establish clear career development paths, provide mentorship, create psychological safety for innovation, and build a culture of operational excellence. Set performance expectations, conduct regular feedback cycles, and develop talent retention strategies aligned with OpenAI's mission.
- Define Data Strategy and Architecture: Establish comprehensive data strategy spanning growth accounting, product partnerships, checkout, billing, payments, revenue, and monetization data subject areas. Design scalable data architectures using modern data stack technologies including dimensional modeling, event modeling, and lakehouse or warehouse approaches. Create technical roadmaps that balance short-term business needs with long-term architectural sustainability.
- Deliver Durable Data Products and Modeling: Own delivery of well-engineered, production-grade data products that connect product behavior, subscription state, checkout events, payment outcomes, and revenue metrics. Implement robust data modeling practices including slowly changing dimensions, fact tables, and event schemas. Ensure data pipelines are reliable, performant, and maintainable across high-volume transaction processing and complex business logic.
- Establish Trusted Metric Definitions and Data Quality Standards: Define authoritative metrics and KPIs for lifecycle, attribution, subscriber acquisition, billing, payments, and revenue that align with financial truth. Implement data quality frameworks including validation rules, anomaly detection, and SLA monitoring. Establish governance policies ensuring consistent metric definitions across product, growth, finance, and executive stakeholders to enable fast, reliable decision-making.
- Partner with Data Science and Product Teams: Collaborate closely with Data Science teams to support experimentation infrastructure, causal measurement, and statistical rigor in A/B testing. Work with Product teams on funnel analysis, user behavior attribution, and scalable self-serve analytics platforms. Enable rapid iteration through accessible, well-documented data contracts and API-first data provisioning approaches.
- Ensure Financial Reconciliation and Compliance: Partner with Finance and Financial Engineering teams to reconcile analytical revenue views with financial statements and production billing systems. Implement audit trails, data lineage tracking, and reconciliation frameworks ensuring financial accuracy. Navigate regulatory requirements around payment processing, subscription accounting, and data privacy.
- Raise Operational Excellence Standards: Establish and enforce operational best practices for critical data pipelines including reliability monitoring, observability instrumentation, privacy-preserving data handling, data governance, and incident response procedures. Implement SLOs, alerting strategies, and postmortem processes. Build runbooks and documentation ensuring knowledge transfer and team resilience.
- Communicate Vision and Roadmap Across Stakeholders: Set clear organizational roadmap with principled tradeoffs between technical debt, feature delivery, and infrastructure investment. Communicate progress, risks, and dependencies to technical teams, product leadership, and business stakeholders. Present data strategy and metrics framework to executive leadership in context of company growth and revenue objectives.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science, Engineering, or Related Field","description":"Educational foundation in computer science, software engineering, mathematics, physics, or related technical discipline. Understanding of fundamental algorithms, data structures, and systems design principles. Equivalent professional experience may substitute for formal degree."}

### technical

- {"name":"Modern Data Systems Architecture","description":"Comprehensive knowledge of cloud data warehouse platforms (Snowflake, BigQuery, Redshift), lakehouse architectures (Delta Lake, Apache Iceberg), and data pipeline orchestration tools (Airflow, Dagster). Understanding of distributed systems, data modeling paradigms (star schema, dimensional modeling, event modeling), and performance optimization for analytics workloads at scale."}
- {"name":"SQL and Python or Scala","description":"Advanced SQL proficiency for complex analytical queries, CTEs, window functions, and optimization. Strong Python or Scala skills for data transformation logic, ETL framework development, and automation scripting. Experience writing production-grade code with testing, documentation, and version control practices."}
- {"name":"Apache Spark and Distributed Computing","description":"Hands-on experience with Apache Spark for large-scale data processing, including RDD operations, DataFrame APIs, and performance tuning. Understanding of partitioning strategies, broadcast joins, and shuffle optimization. Knowledge of when to apply Spark versus SQL-based solutions and distributed computing trade-offs."}
- {"name":"Data Pipeline Orchestration and DevOps","description":"Proficiency with workflow orchestration platforms such as Airflow, dbt (data build tool), or Dagster. Understanding of CI/CD principles applied to data engineering, infrastructure-as-code, containerization with Docker/Kubernetes, and monitoring/alerting systems. Experience implementing data lineage tracking and observability."}
- {"name":"Revenue and Billing Systems Expertise","description":"Deep experience with subscription management platforms, payment processing systems, and revenue recognition frameworks. Understanding of billing cycles, churn metrics, lifetime value calculations, and multi-currency payment flows. Knowledge of revenue accounting standards and reconciliation between operational data and financial statements."}
- {"name":"Growth Metrics and Product Analytics","description":"Expertise in designing and implementing growth accounting systems including cohort analysis, retention metrics, attribution modeling, and funnel analysis. Experience building product analytics data models supporting user journey tracking, feature adoption, and engagement metrics. Understanding of experimentation frameworks and causal inference in product decisions."}

### experience

- {"name":"Leadership of Data Engineering Teams","description":"Minimum 5+ years leading and scaling data engineering teams in fast-moving product or technology environments. Proven track record building high-performing organizations, navigating complex cross-functional dependencies, recruiting and developing engineering talent, and establishing psychological safety for innovation. Experience with team scaling from 3-5 to 10+ engineers."}
- {"name":"Growth, Revenue, and Monetization Data Products","description":"5+ years building trusted growth, lifecycle, subscription, billing, payments, revenue, or monetization data products at meaningful scale (millions of events per day). Demonstrated expertise in connecting product behavior to revenue outcomes, designing metrics that enable business decisions, and owning data quality for high-stakes financial metrics. Experience working with cross-functional finance, product, and engineering teams."}
- {"name":"Data Strategy and Architecture in High-Scale Systems","description":"Proven ability to turn ambiguous business questions into durable data contracts, metric definitions, and multi-year technical roadmaps. Experience designing data systems supporting hundreds of concurrent analytics users, complex transformation logic, and sub-second query performance requirements. Track record of balancing technical excellence with business delivery timelines."}
- {"name":"Cross-Functional Partnership and Stakeholder Management","description":"5+ years building unusually strong partnerships with Data Science, Product, Finance, Financial Engineering, GTM, and Engineering teams. Experience translating between business language and technical requirements, establishing data governance with diverse stakeholders, and gaining buy-in for long-term technical initiatives. Demonstrated excellence in both leading upward and supporting team execution."}

## Skills

### required

- {"name":"SQL and Query Optimization","description":"Advanced SQL proficiency including window functions, CTEs, recursive queries, and performance optimization techniques for analytics workloads"}
- {"name":"Data Modeling and Schema Design","description":"Expertise in dimensional modeling, event data modeling, normalization techniques, and designing scalable data structures for analytics"}
- {"name":"Python or Scala Programming","description":"Production-grade programming skills in Python or Scala for data transformation, pipeline development, and data tooling"}
- {"name":"Distributed Systems and Apache Spark","description":"Understanding of distributed computing principles and hands-on experience with Apache Spark for large-scale data processing"}
- {"name":"Data Pipeline Orchestration","description":"Proficiency with workflow orchestration tools such as Airflow, dbt, Dagster, or similar platforms for building reliable data pipelines"}
- {"name":"Data Warehouse and Lakehouse Platforms","description":"Experience with modern cloud data platforms such as Snowflake, BigQuery, Redshift, or Delta Lake architectures"}
- {"name":"Team Leadership and Development","description":"Proven ability to build, mentor, and scale high-performing engineering teams with focus on psychological safety, clear feedback, and career development"}
- {"name":"Revenue and Monetization Metrics","description":"Deep expertise in growth accounting, subscription metrics, billing systems, and revenue measurement frameworks"}
- {"name":"Data Quality and Governance","description":"Strong commitment to data quality, privacy, security, and implementing governance frameworks for enterprise data systems"}
- {"name":"Cross-Functional Communication","description":"Ability to partner effectively with Data Science, Product, Finance, and Engineering teams; translating between technical and business perspectives"}

### preferred

- {"name":"dbt (Data Build Tool)","description":"Experience with dbt for transformations, documentation, testing, and CI/CD in modern data stacks"}
- {"name":"Event-Driven Architecture","description":"Understanding of event streaming platforms like Kafka, Pub/Sub, or Kinesis and designing event-driven data systems"}
- {"name":"Machine Learning Pipeline Infrastructure","description":"Experience building data systems that support ML/AI workloads, including feature engineering and model serving infrastructure"}
- {"name":"Financial Systems and Reconciliation","description":"Prior experience reconciling operational data with financial systems, working with accounting standards, or payment processing platforms"}
- {"name":"Experimentation and Causal Inference","description":"Familiarity with A/B testing frameworks, experiment design, attribution modeling, and causal inference methodologies"}
- {"name":"Data Security and Privacy Compliance","description":"Knowledge of data privacy regulations (GDPR, CCPA), encryption, access control, and secure data handling practices"}
- {"name":"Product Analytics and Business Intelligence","description":"Background in product analytics, business intelligence platforms, or self-serve analytics tool development"}
- {"name":"Open Source Contributions","description":"Active contributions to data engineering open source projects or demonstrated thought leadership in data systems community"}

## Tech stack

### tools

- {"name":"Apache Airflow","description":"Workflow orchestration platform for scheduling, monitoring, and managing complex data pipelines"}
- {"name":"Dagster","description":"Data orchestration framework with asset-centric approach and built-in lineage tracking"}
- {"name":"Git and Version Control","description":"Source control and collaborative development practices for data infrastructure code"}
- {"name":"Docker and Kubernetes","description":"Containerization and orchestration for deploying and scaling data pipeline infrastructure"}
- {"name":"CI/CD Platforms","description":"Continuous integration and deployment tools for automated testing and deployment of data infrastructure"}
- {"name":"Monitoring and Alerting Systems","description":"Observability platforms for monitoring pipeline health, data quality, and system performance"}
- {"name":"Data Catalog and Lineage Tools","description":"Metadata management and data discovery platforms for documenting data assets and understanding data flow"}

### others

- {"name":"Revenue Recognition Accounting Standards","description":"Knowledge of ASC 606 (GAAP) and IFRS 15 for proper revenue recognition and financial reporting"}
- {"name":"Payment Processing Systems","description":"Understanding of payment gateways, billing platforms, subscription management, and transaction processing"}
- {"name":"Experimentation Infrastructure","description":"A/B testing frameworks, experiment analysis, statistical significance testing, and causal inference methodologies"}
- {"name":"Data Governance and Compliance","description":"Data privacy frameworks (GDPR, CCPA), access control, audit trails, and regulatory compliance"}

### databases

- {"name":"Snowflake","description":"Cloud-native data warehouse platform for analytics, query processing, and data sharing"}
- {"name":"Google BigQuery","description":"Serverless data warehouse for large-scale analytics on petabyte-scale datasets"}
- {"name":"Amazon Redshift","description":"Cloud data warehouse for analytics workloads with columnar storage and MPP architecture"}
- {"name":"Delta Lake","description":"Lakehouse format providing ACID transactions and schema enforcement for data lakes"}
- {"name":"Apache Iceberg","description":"Table format enabling reliable analytics on large volumes of structured data in data lakes"}

### languages

- {"name":"SQL","description":"Primary language for data transformation, analysis, and query optimization on cloud data platforms"}
- {"name":"Python","description":"Data pipeline development, transformation logic, automation scripting, and data tooling"}
- {"name":"Scala","description":"Alternative to Python for distributed data processing and Apache Spark development"}

### frameworks

- {"name":"Apache Spark","description":"Distributed data processing framework for large-scale ETL and analytics workloads"}
- {"name":"dbt (Data Build Tool)","description":"Transformation framework for SQL-first analytics engineering with built-in testing and documentation"}
- {"name":"Dimensional Modeling Frameworks","description":"Data modeling approaches including Kimball methodology, star schemas, and event modeling patterns"}

## Benefits

### benefits

## Compensation

- **max:** 340000
- **min:** 220000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

## Full description
# About the Team

The Applied organization brings OpenAI’s most advanced technology to the world through products like ChatGPT and the APIs that power a growing ecosystem of developer and enterprise applications. Data Engineering builds and operates the trustworthy, secure, and reliable data systems that power decisions across OpenAI.

# About the Role

We’re looking for a Data Engineering Manager to lead the Growth & Revenue data engineering team. This leader will own the data strategy and execution for the data subject areas spanning growth accounting across all product surfaces, product partnerships, checkout, billing, payments, revenue, and monetization, helping OpenAI understand how people adopt, engage with, and pay for our products. You will partner closely with several Data Science, Business, and Engineering partners to connect product behavior to trustworthy subscriber, payment, and revenue measurement.

# In this role, you will:

* Build, manage, and grow a high-performing, inclusive team across the Growth & Revenue data subject areas.
* Define the data strategy for all the data subject areas you own.
* Deliver durable, well-modeled data products that connect product behavior, subscription state, checkout events, payment outcomes, and revenue.
* Establish trusted metric definitions and data quality standards so product, growth, finance, and executive leaders can make fast, consistent decisions.
* Partner with Data Science and Product teams to support experimentation, causal measurement, funnel analysis, and scalable self-serve analytics.
* Partner with Finance and Financial Engineering to ensure analytical revenue views reconcile to financial truth and production billing systems.
* Raise operational excellence for critical pipelines, including reliability, observability, privacy, governance, and incident response.
* Set a clear roadmap, make principled tradeoffs, and communicate progress and risk across technical and business stakeholders.

# You might thrive in this role if you:

* Have deep experience leading and scaling data engineering teams in a fast-moving product or technology environment.
* Bring strong technical judgment across modern data systems, including SQL, Python or Scala, Spark, orchestration, dimensional and event modeling, and lakehouse or warehouse architectures.
* Have built trusted growth, lifecycle, attribution, subscription, billing, payments, revenue, or monetization data products at meaningful scale.
* Can turn ambiguous business questions into durable data contracts, metric definitions, and technical roadmaps.
* Build unusually strong partnerships with Data Science, Product, Finance, Financial Engineering, GTM, and Engineering.
* Care deeply about data quality, privacy, security, and the operational health of systems used for consequential decisions.
* Are an excellent people leader: you hire well, develop talent, give clear feedback, and create an environment where diverse perspectives do their best work.

# What success looks like

* In the first 90 days, you have earned trust with the team and partners, clarified ownership boundaries, assessed the current data portfolio, and aligned on a prioritized roadmap.
* Within a year, Growth & Revenue stakeholders rely on a smaller set of trustworthy, well-owned datasets and metrics for lifecycle, attribution, subscriber, billing, payment, monetization, and revenue decisions.
* The team operates with clear goals, healthy execution rhythms, strong reliability standards, and a hiring and development plan that matches the domain’s ambition.

This role is based in our San Francisco headquarters. We offer relocation assistance for new employees.

**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](https://cdn.openai.com/policies/eeo-policy-statement.pdf).

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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At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.