PHILO: A STREAMING SERVICE FOR TV AND MOVIE LOVERS

At Philo, we’re a group of technology and product people who set out to build the future of television, marrying the best in modern technology with the most compelling medium ever invented — in short, we’re building the TV experience that we’ve always wanted for ourselves. In practice this means leveraging cloud delivery, modern tech stacks, machine learning, and hand-crafted native app experiences on all of our platforms. We aim to deliver a rock solid experience on the streaming basics, while cooking up next generation multi-screen and multi-user playback experiences.

DATA AT PHILO

Data underpins everything we do at Philo: making informed business decisions; analyzing and improving the quality of our streaming experience; running product experiments to optimize our signup flows and improve user journeys; and making it effortless for our users to find the perfect thing to watch. Philo serves over a billion streams to its users every year, generating a wealth of data that we leverage at all levels of the organization. Philo’s data pipeline processes nearly 8 trillion events per year into our petabyte scale data lake, where we run over 30k ETL and BI queries every day to bring data-driven insights to our team.
On the Data Engineering team, we’re looking for people who are comfortable working on complex data infrastructure challenges that support critical business functions across our streaming services. You’ll be working closely with other engineers, data scientists, analysts, and stakeholders to build and deploy scalable data solutions that power our entire service. In addition, you’ll work with departments across the organization to understand their data needs and deliver high-performance, reliable data systems to help the entire team thrive.
We are passionate about building robust, scalable data infrastructure and providing high-quality data infrastructure for the entire company, using both cutting-edge technologies and proven engineering practices in close collaboration with every department. To complete our work, we build on modern tools including AWS-native services, DBT, Segment, Redshift, AWS SageMaker, AWS Glue, Avo, BigEye and more.
Some of the recent projects our Data Engineering team members have worked on include data warehouse cluster upgrades, per-query infra cost optimization, implementation of Apache Iceberg data storage, and evaluating alternative data warehouse technologies.

KEY QUALIFICATIONS:

• 10+ years of professional experience in data engineering, ideally with a few years working on high-volume data platforms
• 3+ years of experience leading data engineering teams in cloud-native environments
• Deep expertise in modern data stack technologies, particularly AWS data services, Redshift, dbt, orchestration tools like Airflow and Dagster, Python, and SQL
• Strong track record of designing and implementing data platforms
• Experience mentoring junior engineers and raising team capabilities through technical documentation and knowledge sharing
• Excellent communication skills with technical and non-technical stakeholders, simplifying complex data concepts for various business audiences
• Experience managing cloud-based data platforms (AWS/GCP/Azure)

IDEAL QUALIFICATIONS:

• Experience in media/streaming industry with understanding of video delivery analytics and metrics
• Knowledge of data modeling and dimensional design
• Experience with data governance and compliance requirements
• Background in implementing data quality frameworks

THE ROLE

We’re seeking an experienced Lead Data Engineer to join our team at Philo. This role will be responsible for designing and implementing our long-term Data Platform Strategy while providing technical leadership to take our data engineering practices to the next level. The ideal candidate will be strongly opinionated about data stack architecture and how to enhance velocity across the entire Data Team at Philo.

RESPONSIBILITIES:

• Lead the design and implementation of Philo’s data platform architecture
• Set technical standards and best practices for data engineering work
• Mentor and coach data engineers in best practices for data pipeline design, performance optimization, and AWS cloud services
• Collaborate with cross-functional teams to understand data platform needs and translate business requirements into technical solutions
• Design scalable data pipelines and infrastructure
• Manage team workload, priorities, and delivery timelines
• Implement data governance, security, and quality control processes
• Drive technical decisions with a focus on long-term sustainability