Senior Data Engineer

ABOUT WAY

Way is building the AI, data, and infrastructure layer for the experience economy.

As artificial intelligence automates labor and people reclaim time, the shift toward spending on experiences over things only accelerates. Way sits at the center of that shift as the platform, intelligence layer, and curated marketplace on which experiences are created, distributed, and sold.

Founded in 2020 and headquartered in Austin with offices in Paris and San Francisco, Way built the category of experiences in hospitality from the ground up, during a pandemic that had shut the industry down. Leading global travel, retail, lifestyle, and hospitality brands leverage Way, including Hyatt, Hilton, Alaska Airlines, Aspen, Piaget, and countless others.

Way’s position is layered by design: a proprietary data network built on millions of experiential data points, the system of record for experiential operations, an e-commerce layer embedded directly into partners’ customer journeys, and a marketplace through Partner Exchange, where brands across hospitality, travel, lifestyle, and retail co-create and co-sell experiences with one another. Every brand that joins makes the network more valuable to every brand already on it.

That combination of data, workflow, distribution, and network effects is not something that can be vibe-coded into existence. It compounds. It is what positions Way as the durable infrastructure for the experience economy regardless of how the technology landscape evolves.

Way is backed with $22m in capital from Tiger Global and strategic family offices and angel investors. The company has mid-8 figures of ARR from a global enterprise customer set, with outstanding growth and efficiency metrics.

We are seeking an experienced Senior Data Engineer to establish and lead our data infrastructure as an early member of our data team. This role will be responsible for building our entire data platform from the ground up, implementing a comprehensive data lake and pipeline architecture, and establishing a culture of data-driven decision-making throughout our organization. The ideal candidate will serve as both a technical leader and data advocate, driving automation excellence while flexing into data science and analytics responsibilities as needed.

• Design and implement a comprehensive data lake architecture using modern cloud-native technologies
• Build scalable ETL/ELT pipelines for real-time and batch data processing across all data sources
• Establish data ingestion frameworks to collect data from application APIs, third-party services, and databases
• Architect automated data quality monitoring, validation, and alerting systems
• Create robust data warehousing solutions optimized for analytics and business intelligence
• Implement Data Ops practices with automated testing and deployment pipelines (CI/CD for data)

• Develop and maintain Python-based data processing frameworks and utilities
• Build an automated data pipeline orchestration using Apache Airflow or similar tools
• Create streaming data processing solutions using Apache Kafka, Kinesis, or Pub/Sub
• Implement infrastructure as code for all data platform components (Terraform, Cloud Formation)
• Establish feature stores and data models that support both operational and analytical workloads
• Optimize data storage costs and query performance across the entire platform
• Collaborate with product and business teams to identify key metrics, KPIs, and analytical requirements
• Build automated reporting dashboards and self-service business intelligence tools
• Support predictive modeling initiatives and A/B testing frameworks

• Minimum 5+ years of hands-on data engineering experience with increasing responsibility
• Preferred 7+ years in data engineering, analytics engineering, or data platform roles
• Proven track record of building data systems from scratch or leading data infrastructure transformations
• Experience working as a solo data engineer or in small, autonomous data teams

• Python proficiency required - demonstrated experience building data pipelines, ETL frameworks, and automation scripts
• SQL expertise - advanced knowledge of complex queries, performance optimization, and data modeling
• Strong experience with cloud platforms (AWS, GCP, or Azure) and their data services
• Proficiency with data lake technologies (Delta Lake, Apache Iceberg, or Apache Hudi)
• Experience with data…