way
Headquartered in Austin, Texas, with its EMEA HQ in Paris, Way is the category‑leading B2B technology platform empowering brands to unlock the power of experiences. In a world where 76% of consumers prefer spending on experiences over material goods, Way enables brands to adapt to this shift with cutting‑edge technology.
Founded in 2020, Way began as a solution for hospitality brands to drive brand loyalty and generate experiential revenue at scale. Industry leaders like Hyatt Hotels, Hilton, Trailborn, and Auberge Resorts Collection rely on Way’s all‑in‑one experiential platform to launch unforgettable experiences — from hot air balloon rides in Mexico City to truffle hunting in the French countryside.
Way has achieved significant milestones, including a $20 million Series A funding round in late 2022, led by Tiger Global and MSD Capital (Michael Dell), at a $100 M valuation. As the company continues its rapid growth, we’re seeking visionary, driven team players to join our dynamic environment, where challenges are met with unmatched rewards as we transform the hospitality and experiences industry globally.
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.
Key Responsibilities
Data Infrastructure & Pipeline Architecture
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 DataOps practices with automated testing and deployment pipelines (CI/CD for data)
Data Engineering & Analysis
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, CloudFormation)
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
Required Experience
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
Required Technical Skills
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 orchestration tools (Apache Airflow, Prefect, Dagster, or similar)
Knowledge of streaming data technologies (Apache Kafka, Kinesis, Pub/Sub)
Familiarity with data warehouse technologies (Snowflake, BigQuery, Redshift, Databricks)
Understanding of containerization (Docker, Kubernetes) and infrastructure as code
Experience with version control systems (Git) and collaborative development workflows
Preferred Qualifications
Background in real‑time analytics and event‑driven architectures
Previous experience in startup or fast‑paced environments
Understanding of data privacy regulations (GDPR, CCPA) and security best practices
Experience with performance monitoring and observability tools
Knowledge of dimensional modeling and data warehouse design patterns
Benefits
Compensation includes a highly competitive salary, generous equity, medical, dental, and vision coverage paid 100% by the company, 401K benefits, and other travel‑related perks
$500 Annual Experience Stipend (Can be used at any of our client partners)
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Founded in 2020, Way began as a solution for hospitality brands to drive brand loyalty and generate experiential revenue at scale. Industry leaders like Hyatt Hotels, Hilton, Trailborn, and Auberge Resorts Collection rely on Way’s all‑in‑one experiential platform to launch unforgettable experiences — from hot air balloon rides in Mexico City to truffle hunting in the French countryside.
Way has achieved significant milestones, including a $20 million Series A funding round in late 2022, led by Tiger Global and MSD Capital (Michael Dell), at a $100 M valuation. As the company continues its rapid growth, we’re seeking visionary, driven team players to join our dynamic environment, where challenges are met with unmatched rewards as we transform the hospitality and experiences industry globally.
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.
Key Responsibilities
Data Infrastructure & Pipeline Architecture
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 DataOps practices with automated testing and deployment pipelines (CI/CD for data)
Data Engineering & Analysis
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, CloudFormation)
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
Required Experience
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
Required Technical Skills
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 orchestration tools (Apache Airflow, Prefect, Dagster, or similar)
Knowledge of streaming data technologies (Apache Kafka, Kinesis, Pub/Sub)
Familiarity with data warehouse technologies (Snowflake, BigQuery, Redshift, Databricks)
Understanding of containerization (Docker, Kubernetes) and infrastructure as code
Experience with version control systems (Git) and collaborative development workflows
Preferred Qualifications
Background in real‑time analytics and event‑driven architectures
Previous experience in startup or fast‑paced environments
Understanding of data privacy regulations (GDPR, CCPA) and security best practices
Experience with performance monitoring and observability tools
Knowledge of dimensional modeling and data warehouse design patterns
Benefits
Compensation includes a highly competitive salary, generous equity, medical, dental, and vision coverage paid 100% by the company, 401K benefits, and other travel‑related perks
$500 Annual Experience Stipend (Can be used at any of our client partners)
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