The Job
Warner Bros. Discovery has established a new Data and Artificial Intelligence (DAI) group to enhance the data-driven capabilities of its streaming products. This team is actively seeking a highly motivated Director of Data Engineering to lead a team of dedicated data engineers and architects. The primary goal is to standardize data products for various stakeholders, with the platform supporting data products for Subscription, Content, Product Analytics, Marketing, and Ad-Sales enablement.
This individual will be instrumental in championing the overall data strategy and architecture, guiding multiple data engineering teams in solving data-driven use cases across HBOMAX, Discovery+, and other digital platforms. The role requires expertise in a wide array of big data processing frameworks (both open source and proprietary), large-scale database systems (OLAP and OLTP), stream data processing, data mart development, BI/Analytics, API Development, and ML Engineering to boost data architecture and data product development efficiency.
The Daily
- Help hire Data Engineers, ML Engineers, and Data Architects to build and maintain various data products.
- Design scalable data products and evangelize their adoption.
- Serve as a Principal Engineer to review designs proposed by various data and ML engineering teams.
- Champion a scalable and cost-effective Data and AI platform, educating the organization on best practices.
- Lead and mentor one or more Data and ML engineering team(s), ensuring productivity, focus, and motivation in a demanding environment.
- Collaborate with higher management and various stakeholders to understand data platform requirements, create roadmaps, and track key milestones.
- Work with the team to architect and implement data products, including data marts, ML frameworks, consumer 360, consumer segmentation, and data integration with first and third-party systems.
- Partner with business units to roll out data product initiatives.
- Present platform architecture, roadmap, plans, status, and risks to stakeholders, including higher management.
- Collaborate with data science teams to operationalize models.
- Champion security and governance, ensuring the data engineering team adheres to company guidelines.
- Take ownership of the platform by enabling 24x7 support.
- Assist in preparing presentations and formal analysis for executive decision-making and strategy development.
- Secure funding for tool/framework operationalization and develop roll-out strategies for platform changes.
- Contribute to resource planning and budget management for platform maintenance.
- Be responsible for cloud cost management and efficiency improvement.
The Essentials
- Bachelor’s degree in computer science or a similar discipline.
- 12+ years of experience in software engineering.
- 7+ years of experience in engineering management.
- 4+ years of experience in data engineering.
- 4+ years of experience in modern ML frameworks.
- Prior experience and deep domain knowledge in Direct To Consumer Business is highly preferred.
- Go above and beyond attitude and readiness to meet business expectations.
- Ability to work in a fast-paced, high-pressure, agile environment.
- Ability to lead one or more teams to deliver high-quality data products.
- Expertise in at least a few programming languages: Java, Scala, Python, or similar.
- Expertise in building and managing large volume data processing (both streaming and batch) platforms is a must.
- Expertise in distributed data processing frameworks such as Apache Spark, Flink, or similar.
- Expertise in OLAP databases such as Snowflake or Redshift.
- Experience with Analytics Tools such as Looker, Tableau, or similar.
- Experience with a variety of data Tools & frameworks (e.g., Apache Airflow, Druid) is a huge plus.
- Expertise in SQL and No-SQL (Apache Cassandra, DynamoDB, MySQL) is a plus.
- Experience in operationalizing and scaling machine models is a huge plus.
- Expertise in stream processing systems such as Kafka, Kinesis, Pulsar, or similar is a plus.
- Expertise in building scalable ML models.
- Experience in building microservices and managing containerized deployments, preferably using Kubernetes, is a plus.
- Cloud (AWS) experience preferred.
- Ability to learn and teach new languages and frameworks.
- Strong interpersonal, communication, and presentation skills.
- Strong team focus with outstanding organizational and resource management skills.