Staff Product Manager, Data Engineering

Liftoff is a leading AI-powered performance marketing platform for the mobile app economy. Our end-to-end technology stack helps app marketers acquire and retain high-value users, while enabling publishers to maximize revenue across programmatic and direct demand.

Liftoff’s solutions, including Accelerate, Direct, Monetize, Intelligence, and Vungle Exchange, support over 6,600 mobile businesses across 74 countries in sectors such as gaming, social, finance, ecommerce, and entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence.

Liftoff is seeking a Senior Product Manager, Data Engineering to lead the roadmap for our data infrastructure and engineering systems that power machine learning and analytics across the organization. This is a highly technical product role requiring a background as a Data Engineer and a strong ability to communicate across both technical and business teams.

The ideal candidate is proactive, technically fluent, and an excellent communicator who can translate complex requirements from analysts and ML engineers into structured, actionable engineering specifications. This position partners closely with Data Engineering, Machine Learning, Data Science, and Analytics teams across both U.S. and Beijing time zones, requiring strong coordination, documentation, and clarity in asynchronous communication.

Key Responsibilities:

• Define and execute the product vision and roadmap for Liftoff’s data infrastructure, ensuring scalability, reliability, and performance.
• Translate complex machine learning and analytics requirements into detailed technical documentation and product specifications for engineering teams.
• Partner with cross-functional teams across the U.S. and Beijing to design, prioritize, and deliver high-impact data pipelines, APIs, and storage architectures.
• Establish and monitor data quality, latency, and reliability KPIs, driving continuous improvement and standardization.
• Collaborate with ML and analytics stakeholders to ensure the data platform enables rapid experimentation and model development.
• Anticipate data and infrastructure needs, proactively shaping architecture and tooling strategy.
• Advocate for data best practices, including governance, lineage, and documentation across global teams.

 

Requirements

• 5–8 years of total experience in data-intensive environments, including:
  • Minimum 2 years as a Data Engineer, building or maintaining large-scale pipelines and distributed data systems.
  • Minimum 3 years in Product Management or Technical Program Management, owning data or infrastructure products.

• Deep understanding of data engineering concepts: ETL frameworks, data modeling, distributed processing, and streaming systems (e.g., Spark, Airflow, Kafka, Flink).
  
  
• Strong familiarity with cloud data platforms (Snowflake, BigQuery, Redshift) and data lake architectures.
  
  
• Proficient in SQL; comfortable understanding Python or similar scripting languages for technical discussions.
  
  
• Demonstrated ability to write clear technical documentation and product specifications.
  
  
• Excellent verbal and written communication, capable of bridging technical and non-technical stakeholders across global teams.
  
  
• Highly proactive and self-directed, able to manage complex cross-time-zone projects with minimal supervision.
  
  

Preferred Experience:

• Background in ad tech, mobile marketing, or large-scale programmatic data systems.
  
  
• Experience with model training pipelines, feature stores, or data observability tooling.
  
  
• Knowledge of data governance, schema evolution, and metadata management practices.
  
  
• Experience collaborating with distributed teams across U.S. and Asia time zones.

Why Join Liftoff?

• Innovative Environment: Be part of a company at the forefront of mobile app marketing, utilizing cutting-edge technologies to drive results.​
• Professional Growth: Opportunities for continuous learning and career advancement in a dynamic industry.​
• Collaborative Culture: Work with a diverse team of talented p