Senior Data Analytics Engineer

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 D2C brands (not an agency), with a presence in Walmart, Nordstrom, Amazon, and a strong global 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

This role sits at the intersection of data engineering, analytics, business intelligence, and machine learning infrastructure. You will architect and scale modern data pipelines, build resilient data models, and ensure the reliability, accuracy, and operational performance of our BI reporting layer and ML platforms.

Key Responsibilities

Pipeline & System Architecture: Architect, scale, and maintain end-to-end ETL/ELT pipelines and Airflow-driven workflows across the full data lifecycle (extraction transformation ML modeling reporting).

Data Modeling & BI Delivery: Design and optimize SQL transformations, datasets, and high-quality data models. Build, centralize, and maintain dashboards and analytical tools to translate business needs into scalable BI solutions.

Data Quality & Governance: Establish strong governance, monitoring, alerting, SLAs, data validation, and anomaly detection. Perform root-cause analysis to ensure high accuracy, reliability, and business trust in metrics.

Machine Learning & Analytics Support: Operationalize ML models in batch/real-time environments and build internal data tools to empower Marketing Science, Analytics, and commercial teams.

Performance & Cost Optimization: Optimize complex SQL queries and large-scale datasets for performance, cost-efficiency, and scalability across the AWS cloud ecosystem.

Stakeholder Collaboration: Partner with cross-functional teams to define and report on core business metrics (e.g., CAC, ROAS, LTV, conversion funnels) to directly guide executive decision-making.