Sr. Data Engineer I

iHerb is a global online health and wellness retailer focused on making healthy living accessible while innovating its shopping experience. The Senior Data Engineer will scale the company’s cloud-based data ecosystem, including data lakes, warehouses, and machine-learning enablement platforms, while partnering with data scientists and cross-functional teams to deliver reliable data and production-grade MLOps solutions.

Responsibilities:

• Designs and builds scalable data extracts, integrations, transformations, and data models
• Ensures successful deployment and provisioning of data solutions across required environments
• Designs and implements data architectures and applications that enable speed, quality, and operational efficiency
• Interacts with cross-functional stakeholders to gather and define requirements and translate them into technical designs
• Develops deep familiarity with enterprise datasets, builds domain knowledge, and advances data quality
• Reviews requirements, identifies gaps, and drives resolution with stakeholders
• Identifies and recommends continuous improvement opportunities, ensuring integrations are automated, governed, and observable
• Serves as a key team member in designing and deploying a ground-up cloud data platform and pipeline
• Partners with data scientists to design, build, and maintain reproducible machine-learning pipelines, including feature engineering, model training, validation, deployment, and monitoring
• Implements CI/CD for data and ML workflows (model packaging, automated testing, environment management, release automation)
• Builds and maintains production-grade ML infrastructure such as feature stores, model registries, data versioning, and experiment tracking frameworks (e.g., MLflow)
• Ensures ML models follow best-practice governance, including automated model performance monitoring, drift detection, logging, observability, and alerting
• Designs scalable data pipelines optimized for ML workloads, such as batch, streaming, and real-time inference use cases
• Establishes MLOps standards, coding practices, and automation patterns that scale across teams

Requirements:

• • Bachelor or Master`s degree in technical discipline such as Computer Science, Information Systems or another technical field
• • People person, team player with a strong can-do mentality
• • 5+ years of experience as a Data Engineer within a data and analytics environment
• • Strong interpersonal skills with a collaborative, proactive, and solution-driven mindset
• • Proficiency in data modeling concepts and techniques
• • Expertise with Databricks and other cloud data warehousing solutions such as S3, Redshift, or BigQuery
• • Hands-on experience building data pipelines and ETL/ELT workflows using PySpark for semi-structured data (merge, delete, combine, wrangling)
• • Advanced knowledge of Python and advanced working SQL skills including query optimization
• • Ability to write, test, and debug RESTful APIs
• • Experience working in agile, cross-functional environments
• • Strong analytical, problem-solving, and critical-thinking capabilities
• • Ability to guide junior engineers and contribute to technical design reviews
• • Strong communication skills with the ability to present complex concepts clearly
• • Experience in data quality initiatives such as Master Data Management (MDM)
• • Experience operationalizing machine-learning models in production environments
• • Hands-on experience with ML tooling such as MLflow, SageMaker, Databricks ML, Kubeflow, or similar
• • Experience implementing CI/CD pipelines for data and ML workloads, including automated testing, deployment pipelines, and environment configuration
• • Understanding of model lifecycle management, data versioning, feature store design, and model monitoring concepts
• • Experience containerizing ML workloads using Docker and deploying them via cloud-native services or orchestrators
• • Familiarity with monitoring frameworks, experiment tracking, and performance observability for ML models
• • Databricks certifications – Associate/Professional
• • AWS Certified Solutions Architect – Associate/Professional
• • AWS Certified Developer – Associate/Professional
• • AWS Certified DevOps Engineer
• • AWS Certified Solutions Architect
• • AWS Certified Data Analytics
• • AWS Certified Security - Specialty
• • AWS Certified Cloud Practitioner
• • DevOps experience with CICD & unit/integration testing, Docker containerization, workflow orchestration