We're hiring a Backend Engineer who will architect and maintain the back-end services and data pipelines that power our platforms - and you'll turn the data flowing through them into the metrics and analysis that guide our decisions. Engineer, data engineer, and data scientist all in one seat. This is a unique, high-growth opportunity. As AI reshapes how software and data work together, the people who can both build robust infrastructure and reason rigorously about what the data says are extraordinarily valuable. You'll have real ownership, direct influence on strategy through the metrics you define, and room to grow as our data and platforms scale to serve millions.
What You'll Do
Design, develop, and deploy scalable back-end services, APIs, and database architectures.
Take end-to-end ownership of technical projects, from technical design to production release.
Write clean, maintainable, well-tested code, and troubleshoot and debug across the stack.
Optimize performance, scalability, and security, and implement robust logging and observability.
Engineer the dataBuild and maintain reliable data pipelines, from raw ingestion to reporting.
Design and optimize data infrastructure and warehousing solutions.
Ensure data quality, governance, and consistency across systems.
Implement logging and instrumentation that make our products measurable.
Drive insight and metricsPartner with leadership and product to define, set, and track our goal metrics.
Build dashboards and visualizations accessible to both technical and non-technical stakeholders.
Design experiments and analyze A/B tests to optimize features and experiences.
Build predictive models and recommendation systems that personalize and strengthen user journeys.
Translate complex analysis into clear, practical recommendations that inform decisions.
What You Bring
3+ years in back-end or full-stack engineering, with meaningful data science or analytics experience (or the reverse).
Strong proficiency in a server-side language (Python preferred; Node.js, Ruby, or Java also welcome) and building RESTful APIs.
Strong SQL, plus experience working with both SQL and NoSQL databases.
Strong ability to set goal metrics for a team and track progress over time.
Proven experience building and maintaining data pipelines.
Experience defining and tracking metrics, plus logging and instrumentation.
Experience setting up and running experiments to measure impact.
Data visualization skills and the ability to communicate analysis in a clear, compelling way.
A self-starter who thrives with autonomy and strong problem-solving skills and intellectual curiosity.
Excellent cross-functional collaboration and communication skills.
A bachelor's degree in Computer Science, Data Science, Data Engineering, or a related field, or equivalent practical experience.
A deep commitment to Lean In's mission to inspire and equip women to lead.
Alignment with the Sandberg Bernthal Family Foundation's initiatives, including building resilience and advancing equality.
The Sandberg Goldberg Bernthal Family Foundation is committed to Equal Employment Opportunity without regard to age, race, ethnicity, gender, protected veteran status, disability, sexual orientation, gender identity, or religion.
It's important to us that people of all identities and backgrounds see themselves represented and included in our work, so we actively encourage people of all identities and experiences to apply.
Before you apply, please review our approach to AI in the hiring process here.
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
