Summary
We are looking for a hands-on Senior Data Engineer with experience building scalable ETL and data platforms in modern cloud environments.
In this role, you will help design and implement the next generation of our data pipelines, enabling our platform to process and analyze growing volumes of ticketing, market, and consumer data.
Our existing pipelines contain significant domain expertise and business logic that power our analytics platform. You will work to translate and evolve this logic into a scalable, maintainable architecture using modern engineering practices.
This is a high-impact role with significant ownership over how data moves through our platform and supports the machine learning systems that drive our products.
Job Function
- Design and implement the next generation of our core data pipelines
- Translate existing pipeline logic into a clean, maintainable architecture
- Develop data pipelines in Python using modern best practices
- Improve pipeline observability, logging, and failure recovery
- Work with AWS services such as S3, Lambda, ECS, RDS, and/or Redshift
- Design and implement data validation, monitoring, and alerting
- Collaborate closely with leadership to define the future data architecture
- Document pipeline design and operational workflows
Our Tech Stack
- Python
- Apache Airflow
- AWS (S3, ECS, Lambda, RDS, Redshift)
- Docker
- PostgreSQL
What Success Looks Like
- By the end of the engagement, you will have helped us:
- Deliver the next generation of our core ETL pipelines with a scalable and maintainable architecture
- Implement robust data validation, monitoring and observability across the data platform
- Enhance pipeline reliability, performance and operational visibility
Requirements
Required Experience
- 7+ years of software or data engineering experience
- Proven experience designing and building ETL/data pipelines from scratch
- Strong expertise with Python for data engineering
- Deep familiarity with AWS data infrastructure
- Experience building production-grade pipelines with logging, monitoring, and error handling
Nice to Have
- Experience evolving existing data pipelines into scalable, production-grade data platforms
- Experience designing data quality / validation frameworks
- Familiarity with data observability and SLAs
- Experience with analytics or sports data
- Experience working in startup or small team environments
Benefits
Why This Role Is Interesting
- High ownership and architectural influence
- Work directly with engineering leadership
- Small team with fast decision making
- Potential to convert to a full-time leadership role