Data Engineer
Senior Data Engineer focused on building scalable BI pipelines and data models. Designs, implements, and maintains ETL workflows using Python, SQL, Airflow, and Snowflake on AWS, delivering actionable insights through Looker or Tableau for eCommerce growth.
About Trafilea
Trafilea is a Consumer Tech Platform for Transformative Brand Growth. We’re building the AI Growth Engine that powers the next generation of consumer brands.
With over $1B+ in cumulative revenue, 12M+ customers, and 500+ talents across 19 countries, we combine technology, growth marketing, and operational excellence to scale purpose-driven, digitally native brands.
We own and operate our own digitally native brands (not an agency), with presence in Walmart, Nordstrom, and Amazon, and a strong global D2C footprint.
Why Trafilea
We’re a tech-led eCommerce group scaling our own globally loved DTC brands, while helping ambitious talent grow just as fast.
🚀 We build and scale our own brands.
🦾 We invest in AI and automation like few others in eCom.
📈 We test fast, grow fast, and help you do the same.
🤝 Be part of a dynamic, diverse, and talented global team.
🌍 100% Remote, USD competitive salary, paid time off, and more.
Job Responsibilities
We’re looking for a Senior Data Engineer to architect and scale the data infrastructure behind our Machine Learning platform. You won’t just maintain pipelines. You’ll design the systems that turn billions of data points into production-ready models powering global consumer brands.
Architect and scale advanced ETL pipelines using modern Big Data technologies
Design resilient data frameworks across the full lifecycle: extraction → transformation → ML modeling
Lead the development of Airflow-driven data workflows
Optimize large-scale datasets and complex SQL queries
Operationalize machine learning models in batch and real-time environments
Improve cost-efficiency and scalability across our AWS data ecosystem
Elevate data quality standards with strong governance and monitoring systems
Build internal data tools that empower Marketing Science & Analytics teams
Requirements
2–3+ years as a Data Engineer or ML Engineer in production environments
You think in systems, not scripts
You care about scalability, cost-efficiency, and precision
You reject mediocrity — performance and reliability matter
You document your work and raise the bar for quality
The Tech Stack You’ll Master
AWS ecosystem : S3, Glue, Athena, Redshift, Lambda, EC2, RDS, EMR, VPC, ECS/EKS
Apache Airflow for orchestration
Python (object-oriented programming)
Advanced SQL optimization
CI/CD with GitHub Actions or GitLab
Docker & Kubernetes (EKS/ECS, ECR)
Originally posted on Himalayas
Posted June 25, 2026