Key skills
Data EngineeringCloud Data WarehousingMachine Learning OperationsETL/ELT WorkflowsData ModelingPythonSQLRESTful APIsCI/CD PipelinesDockerAnalytical SkillsAIMLMLOpsMLflowKubeflowETLELTData WarehousingAnalyticsBigQueryRedshiftDatabricksIntegration TestingAWSS3SageMakerRESTfulAgileCI/CDCommunication
About this role
iHerb is on a mission to make health and wellness accessible to all, and they are seeking a Senior Data Engineer to help evolve and scale their modern data ecosystem. This role will contribute to the company's data-driven culture and help advance MLOps capabilities to support production-grade AI/ML initiatives.
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
- 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
- Highly Desired AWS Certifications (any)
- DevOps experience with CICD & unit/integration testing, Docker containerization, workflow orchestration
- 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