About
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 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 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
Languages
- English
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