Data Engineer II (with MLOps)
About the Marketing Engine team:
The marketing team handles comprehensive 360º communication and comprises over 150 people. We manage all aspects in-house and operate a robust automation engine. Our combined monthly traffic across all brands exceeds 31 million. As a marketing team member, you'll play a crucial role in the upcoming phase of our brand's growth as we expand and introduce new products to the market.
Design and maintain scalable batch data pipelines in AWS to power analytics and ML use cases.
Develop and optimize SQL transformations and analytical datasets for BI and predictive workloads.
Build reliable ETL/ELT processes with monitoring and data quality checks.
Create feature-ready datasets and support feature engineering pipelines for ML initiatives.
Deliver production-grade data to support elasticity modeling and advanced performance analytics.
Design data infrastructure for A/B testing and measurable experimentation.
Develop ingestion pipelines for marketing and campaign analytics.
Contribute to CI/CD-driven MLOps workflows for model deployment and monitoring in AWS.
Collaborate on data governance, cost optimization, and scalable architecture decisions.
Enable integration of AI and LLM-powered capabilities through robust, future-ready data services.
A Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related technical field — or equivalent practical experience
2-4+ years in Data Engineering, Analytics Engineering, or a backend data-focused role
Hands-on experience designing and maintaining data pipelines and data warehouse solutions in AWS
Strong SQL — efficient transformations, query optimization, and analytical data modeling
Proficiency in Python for data processing and pipeline development
Practical experience with ETL/ELT processes, data warehousing concepts (dimensional modeling), and data quality best practices
Familiarity with core AWS services such as S3, Redshift, Lambda, and CloudWatch
Awareness of ML data preparation and feature engineering workflows — you don't need to build models, but you'll support the people who do
Strong analytical thinking, clear communication, and a collaborative mindset across distributed teams
Fluent English, written and spoken
Experience contributing to MLOps workflows and CI/CD for ML models.
Exposure to A/B testing infrastructure and experimentation frameworks.
Familiarity with AI/LLM integration in product environments.
Experience in marketing analytics or campaign data pipelines.
A proactive mindset with a strong sense of ownership and curiosity about emerging AI trends.
