Senior Data Engineer (BI)
WFA Digital Insight
Trafilea’s senior data engineer slot sits at the heart of an AI‑driven growth engine that powers a portfolio of consumer‑facing brands. Unlike a typical data‑pipeline job, the role demands designing end‑to‑end frameworks that feed billions of data points into production‑ready machine‑learning models. Candidates will be expected to own both the architectural blueprint and the day‑to‑day reliability of massive AWS data services, from S3 storage to Redshift warehouses. The team sits alongside marketing science and analytics groups, meaning the engineer must translate raw data into actionable insights for brand‑level decisions. Success hinges on a blend of system thinking, cost‑conscious scaling, and rigorous data‑governance—all while contributing internal tooling that accelerates the broader organization’s experimentation cadence.
Job Description
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 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)
How to Stand Out
- Highlight concrete examples of Airflow DAGs you’ve built, including the scale and frequency of runs.
- Include a short portfolio or GitHub repo showing Python scripts that interact with AWS services.
- Prepare to discuss how you’ve reduced cloud costs through architecture changes or query optimizations.
- Be ready to walk through a data‑quality issue you identified, the monitoring you set up, and the resolution process.
- Emphasize remote‑work discipline: show how you stay organized, communicate across time zones, and manage hand‑offs.
- When negotiating, reference the USD‑based compensation model and ask about any performance‑based bonuses or equity.
- Watch for vague promises about “flexible hours” without clear expectations on overlap with core team meetings; clarify required synchronous time.
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