TIDAL is a global music and entertainment streaming platform committed to creating a deeper connection between artists and fans through its library of more than 80 million songs, over 350,000 high quality videos, and available in over 60 countries. In addition, TIDAL offers its subscribers exclusive access to high-profile music and music videos, original content series, podcasts, documentaries, livestream concerts, tickets, merchandise and live experiences. Together, TIDAL and Block will be music-obsessed and artist-focused while we explore new artist tools, listener experiences, and access to financial systems that help artists be more successful.
We are seeking data engineers to join us in building our streaming and artist platforms. Data is an integral part to the Personalization team’s success. As a data engineer you will be responsible for delivering the datasets needed to evolve our features and deliver better experiences to artists and TIDAL’s users.
The primary purpose of the Personalization team is to connect fans and artists in meaningful ways. Machine learning is the main driver of personalization in our team. We want every fan and artist to have their own individual version of TIDAL, each with their own unique homepage, personalized playlists, and recommendations.
You will report to the personalization lead
- Own and operate the data pipelines generating a number of core datasets for the Personalization team
- Build datasets and dashboards that enable the product and engineering to better understand the impact of our work
- Work with data processing frameworks, technologies and platforms like Databricks and Apache Spark
- Work with a cross functional team machine learning engineers, software engineers and product managers to build new technologies and features
- Work with our Data Protection Officer to ensure that all data is compliant with GDPR, CCPA, and PII
- Initiate, influence and drive technical projects within TIDAL
- Support existing processing running in production
- 3-5 years of hands-on experience building scalable data pipelines (batch and/or streaming)
- You are fluent in Python
- Experience working with cloud platforms like AWS
- Experience developing orchestration/scheduling jobs using tools like Airflow
- Strong knowledge about data modeling, data access and data storage techniques
- A strong understanding of system design, data structures and algorithms
- Good communication and cooperation skills
- Airflow or other job orchestration tools
We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is a proud equal opportunity employer. We work hard to evaluate all employees and job applicants consistently, based solely on the core competencies required of the role at hand, and without regard to any legally protected class.
We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we’re doing to build a workplace that is fair and square? Check out our I+D page.
Block, Inc. (NYSE: SQ) is a global technology company with a focus on financial services. Made up of Square, Cash App, Spiral, TIDAL, and TBD, we build tools to help more people access the economy. Square helps sellers run and grow their businesses with its integrated ecosystem of commerce solutions, business software, and banking services. With Cash App, anyone can easily send, spend, or invest their money in stocks or Bitcoin. Spiral (formerly Square Crypto) builds and funds free, open-source Bitcoin projects. Artists use TIDAL to help them succeed as entrepreneurs and connect more deeply with fans. TBD is building an open developer platform to make it easier to access Bitcoin and other blockchain technologies without having to go through an institution.