Fandango is seeking a Director of Data Engineering to lead the development and maintenance of data systems that support decision-making, monetization, and privacy-safe marketing intelligence for movie studios, through the Core Data Engineering team and the Fandango360 B2B marketing data platform.
Requirements
- BS in Computer Science, Engineering, or related field (or equivalent practical experience).
- 10+ years in data engineering roles working with big data, ETL pipelines, and orchestration.
- 5+ years leading technical teams (Data Engineering, Application Engineering, or Platform Engineering).
- Working knowledge of cloud technologies, infrastructure, and technical security fundamentals.
- Deep hands-on experience with: AWS Glue, MWAA (Managed Workflows for Apache Airflow).
- AWS Lambda, API Gateway, Cognito.
- Batch orchestration with AWS Batch and Step Functions.
Responsibilities
- Drive measurable improvements in data availability, platform reliability, and time-to-insight across product and marketing use cases.
- Own the vision and roadmap for Core Data Engineering and the Fandango360 SaaS platform.
- Act as technical lead and co-owner of the Fandango360 product roadmap, working closely with Product Management to deliver value to studio clients.
- Collaborate with Product, Data Science, and Business stakeholders to deliver scalable, compliant data products.
- Drive innovation in clean room technology, marketing data activation, and advanced use of first-party data.
- Architect solutions for data ingestion, orchestration, warehousing, and analytics using AWS-native services.
- Oversee integrations with internal and external platforms, including social, ad networks, and transactional systems.
Other
- Lead, mentor, and scale a team of approximately 10-15 engineers.
- Foster a high-performance, self-directed, and accountable culture focused on quality, velocity, and continuous improvement.
- Partner with Privacy, Legal, and Data Governance teams to ensure ethical and compliant data use.
- Support deployment of ML models (e.g., recommendation systems, customer segmentation, forecasting) into production pipelines.
- Fully Remote: This position has been designated as fully remote, meaning that the position is expected to contribute from a non-NBCUniversal worksite, most commonly an employee’s residence.
