Engineering Manager, Data Platform & ML Ops
Bimbingi tim data platform dan ML Ops untuk mengembangkan infrastruktur yang han
Kamu akan memimpin tim yang bertanggung jawab atas data foundation Fingerprint, mulai dari data warehouse internal hingga lifecycle ML Ops. Tim ini mendukung business intelligence dan product analytics. Kamu akan memastikan infrastruktur yang handal dan skalabel untuk produk identifikasi dan smart signals Fingerprint.
Kenapa Menarik?
Bergabung dengan perusahaan yang fokus pada open-source dan didukung oleh investor besar
Tanggung Jawab Utama
- Membimbing dan mentoring tim 4-6 engineer yang berfokus pada data platform dan ML operations
- Mengelola kehandalan, skalabilitas, dan evolusi data warehouse internal Fingerprint
- Mengawasi seluruh lifecycle ML Ops mulai dari eksperimen hingga deployment model
Persyaratan
- Pengalaman memimpin tim engineer
- Paham tentang data warehouse dan ML operations
- Kemampuan untuk memastikan infrastruktur yang handal dan skalabel
Skills Wajib
Keywords
Lihat Deskripsi Asli dari 4dayweek.io
Deskripsi asli dari 4dayweek.io
**Fingerprint** empowers developers to stop online fraud at the source. We work on turning radical new ideas in the fraud detection space into reality. Our products are developer-focused and our clients range from solo developers to publicly traded companies. **We are a globally dispersed, 100% remote company** with a strong open-source focus. Our flagship open-source project is [FingerprintJS](https://github.com/fingerprintjs/fingerprintjs) (27K stars on GitHub). [We have raised $77M](https://www.crunchbase.com/organization/fingerprintjs) and are backed by Craft Ventures (previously invested in [Tesla,](https://www.tesla.com/) [Facebook,](https://facebook.com/) [Airbnb](https://www.airbnb.com/)), Nexus Venture Partners (previously invested in [Postman](https://www.postman.com/), [Apollo.io,](https://www.apollo.io/) [MinIO](https://min.io/), Druva) and Uncorrelated Ventures (previously invested in [Redis,](https://redis.io/) [Rollbar](https://rollbar.com/)& [Gradle](https://gradle.org/)). _We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Fingerprint recruiting email communications will always come from the @fingerprint.com domain. Any outreach claiming to be from Fingerprint via other sources should be ignored._ * * * ## Engineering Manager, Data Platform & ML Ops We are looking for an Engineering Manager to join our Data Platform & ML Ops team. In this role, you will lead the team responsible for Fingerprint's data foundation — from our internal data warehouse that powers business intelligence and product analytics, to the full ML Ops lifecycle that turns raw signals into production models. You'll foster a culture of high performance, helping engineers grow while delivering the reliable, scalable infrastructure our identification and smart signals products depend on. We believe that diverse perspectives fuel innovation, and we encourage candidates from all backgrounds and experiences to apply. ### **What You’ll Do** - Lead and mentor a team of 4-6 engineers spanning data platform and ML operations. - Own the reliability, scalability, and evolution of Fingerprint's internal data warehouse — the foundation for business analytics and a direct input to our flagship identification and smart signals products. - Oversee the full ML Ops lifecycle end-to-end: experimentation, training pipelines, model deployment, and production monitoring. - Provide technical leadership by collaborating with senior engineers, guiding architecture decisions, and reviewing complex technical proposals. - Work closely with data scientists, product managers, data analysts and engineering leads to translate data and ML investments into measurable product outcomes. - Coach and support engineer growth, promoting continuous learning across a fast-moving data and ML landscape. - Define and evolve platform standards, tooling, and best practices across both domains. ### **Requirements:** - Minimum of 2 years of experience leading data engineering, ML engineering, or platform teams in an agile environment. - At least 5 years of professional experience in data engineering, ML engineering, or adjacent software engineering, particularly within SaaS. Hands-on experience in both data infrastructure and ML systems is a must — you don't need to be an expert in both, but you should be technically credible on both sides of the house. - Strong technical background across data infrastructure and ML systems. - Experience managing engineers across multiple technical disciplines. - Proven ability to lead teams shipping high-reliability data products that prioritize quality and user impact. - Demonstrated success driving change and innovation in fast-paced, scaling environments. ### **Preferred Qualifications:** - Experience leading teams in a startup or high-growth environment. - Familiarity with analytical storage systems such as Click
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