Scribd
San Francisco
201-500 employees
The best audiobooks, books, and more, available anywhere. Join us on our mission to change the way the world reads.
Other Open Roles
Prior Listings
- Machine Learning Engineer - Recommendations
- Machine Learning Engineer
- Senior Product Manager, Content Data
- Senior Manager Data Engineering
- Director of Applied Research / Data Science
- Product Analyst, Account
- Senior Machine Learning Engineer (MLOps) - Applied Research
- Senior Machine Learning Engineer - Recommendations
- Senior Backend Engineer (Data pipeline and Metadata)
- Data Platform Engineer
- Senior Data Engineer
- Senior Backend Engineer (Data pipeline)
- Data Scientist
- Senior Applied Research Analyst
- Data Operations Manager
- Senior Manager Machine Learning
- Sr. Machine Learning Engineer
- Data Engineering Manager
- Data Analyst
- Senior Product Analyst/ Senior Data Scientist, Advertising
Data Platform Engineer
Data Platform Engineer
This job is no longer open
At Scribd (pronounced “scribbed”), we believe reading is more important than ever. Join our cast of characters as we build the world’s largest and most fascinating digital library: giving subscribers access to a growing collection of ebooks, audiobooks, magazines, documents, and more. In addition to works from major publishers and top authors, we also create our own original content exclusively for Scribd users. Our community includes over 1M subscribers in more than 190 countries. Join us in turning screen time into quality time!
Scribd
/skribbed/ (n).
1. a tech company changing the way the world reads
2. a membership that gives users access to the world’s largest online library of books, audiobooks, sheet music, news, and magazines
We value trying new things, craftsmanship, being an open book, and the people that make our team great.
Join us and build something meaningful.
About the team
Simply put, Core Platform is here to provide robust and foundational software, increasing operational excellence to scale apps and data at Scribd.
Our primary customer is Scribd Engineering. We are focused on building, testing, deploying apps and infrastructure which will help other teams rapidly scale, inter-operate, integrate with real-time data, and incorporate machine learning into their products. Working with our customers in the Data Science and Content Engineering, and our peers in Internal Tools and Infrastructure teams we bring systems-level visibility and focus to our projects.
We will develop and operate standards and infrastructure for RPC, service discovery, and data ingestion.
We will be building backend systems which enable Scribd Engineering to aid our product's growth on continued success. Our goal is not total architectural or design perfection, but rather choosing the right trade-offs to strike a balance between speed, quality, and cost. We will also be accountable for education and evangelism of our work within Scribd Engineering, this includes writing thorough documentation for the systems we build, hosting internal workshops, and providing implementation assistance to our peers across engineering.
You will
- Define, build, and deploy a new, comprehensive, and cross-team data platform.
- Adapt existing organically-grown systems to a more thoughtful architecture for ingesting, processing, and re-incorporating content and behavioral data streams into Scribd's products.
- For some projects this may entail implementing new Spark-based applications, but for others it may involve updating Ruby code, which generates or processes inbound events from clients.
You have
- Data storage expertise - Our current data stores include: MySQL, Elasticsearch, Redis, Hive, HDFS. Candidates should have a strong working knowledge of building non-trivial applications utilizing at least 2+ of the given data storage technologies.
- Must have a strong grasp of the types of problems where relational data stores, document stores, and object stores should be used.
- Spark/Kafka expertise - Strong knowledge of how to architect and building streaming applications and the systems, which work collectively to back them
- Experience with similar tools such as Storm, RabbitMQ, or other queueing/stream processing tools
Ideally you have
- Comprehension of how to bring machine learning models from development to production
- Working knowledge of how developers and data scientists develop machine learning models.
Why we work here
• Our HQ is in SF, but we have teams distributed in Toronto, Amsterdam, and remote engineering throughout the US
• Health benefits: 100% employer covered Medical/Dental/Vision for regular, full-time employees• Generous PTO policy plus we close for the last week in December
• 401k matching
• Paid Parental leave
• Monthly wellness budget
• Professional development: generous annual budget for our employees to attend conferences, classes, and other events
• Apple laptops and any equipment you want to customize your work station
• Free Scribd membership and a yearly reading stipend!
• Company events that include monthly happy hours and offsites (past events include Santa Cruz, bowling, arcades, geocaching, ropes courses, etc.)
In the meantime, check out our office and meet some of the team at https://www.scribd.com/about
Scribd values diversity, and we make all hiring and employment selections based on merit, qualifications, expertise, talent, and contribution, not who you are by choice or circumstance. We value the people who make Scribd a great place to work and strive to create an environment where your work is recognized and personhood respected.
This job is no longer open