Senior Data Analytics Engineer
Trafilea
WorldwideremotePosted 1 month ago
Skill Required
Data-EngineeringAnalytics-EngineeringBusiness-IntelligenceMachine-Learning-EngineeringSenior-Data-Analytics-EngineerAnalytics-EngineerSenior-Data-AnalyticsSenior-Data-And-Insights-EngineerData EngineerAnalytics EngineeranalyticsMachine Learningdata engineeringDockerGitHub ActionsAirflowEngineeringR ProgrammingKubernetesanalyticalautomationObservabilitydesigningsimilar)RedshiftbuildingTableauPythondesignGitCI/CDCloudRustPerformance MarketingdbtFulltime
Key highlights
- 100% Remote
- USD competitive salary
- 4+ years experience required
- AWS cloud ecosystem experience required
Role overview
Trafilea is a Consumer Tech Platform for Transformative Brand Growth, building an AI Growth Engine that powers next-generation consumer brands. With over $1B+ in cumulative revenue, 12M+ customers, and 500+ talents across 19 countries, they combine technology, growth marketing, and operational excellence to scale purpose-driven, digitally native brands. They own and operate their own D2C brands (not an agency), with a presence in Walmart, Nordstrom, Amazon, and a strong global footprint. This remote role sits at the intersection of data engineering, analytics, business intelligence, and machine learning infrastructure, focusing on architecting and scaling modern data pipelines, building resilient data models, and ensuring reliability of BI reporting and ML platforms.
Responsibilities
- Architect, scale, and maintain end-to-end ETL/ELT pipelines and Airflow-driven workflows across the full data lifecycle (extraction → transformation → ML modeling → reporting)
- 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
- 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
- Operationalize ML models in batch/real-time environments and build internal data tools to empower Marketing Science, Analytics, and commercial teams
- Optimize complex SQL queries and large-scale datasets for performance, cost-efficiency, and scalability across the AWS cloud ecosystem
- 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
Requirements
- 4+ years in Data Engineering, Analytics Engineering, BI, or ML Engineering in production environments
- Advanced SQL proficiency (joins, CTEs, window functions, optimization) and proven experience designing/maintaining production data models and pipelines
- Hands-on experience with core AWS data services (e.g., Redshift, S3, Glue, Athena, Lambda)
- Hands-on experience with Apache Airflow for workflow management
- Strong Python skills (OOP focus), experience with CI/CD practices (GitHub Actions/GitLab), and containerization (Docker, Kubernetes/ECS/EKS)
- Proficiency with BI platforms (Tableau, QuickSight or similar) and direct ownership of production reporting, data quality, and root-cause analysis
- Systems-level thinker with high standards for documentation, scalability, precision, and communicating insights to technical and non-technical partners
Nice to have
- Experience with dbt or modern ELT frameworks
- Specialized e-commerce and marketing analytics expertise (CAC, ROAS, LTV, retention, and funnel optimization)
Benefits
- 100% Remote
- USD competitive salary
- Paid time off
Additional details
- Trafilea is a Consumer Tech Platform for Transformative Brand Growth
- Building the AI Growth Engine that powers the next generation of consumer brands
- Over $1B+ in cumulative revenue
- 12M+ customers
- 500+ talents across 19 countries
- Combine technology, growth marketing, and operational excellence to scale purpose-driven, digitally native brands
- Own and operate our own D2C brands (not an agency)
- Presence in Walmart, Nordstrom, Amazon, and a strong global footprint
- Role sits at the intersection of data engineering, analytics, business intelligence, and machine learning infrastructure
- Building, centralizing, and maintaining dashboards and analytical tools to translate business needs into scalable BI solutions
- Establishing strong governance, monitoring, alerting, SLAs, data validation, and anomaly detection
- Operationalizing ML models in batch/real-time environments
- Building internal data tools to empower Marketing Science, Analytics, and commercial teams
- Optimizing complex SQL queries and large-scale datasets for performance, cost-efficiency, and scalability across the AWS cloud ecosystem
- Partnering 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
- Originally posted on Himalayas