Job description

For A Better You At iHerb, we believe that living a healthy and balanced life should be easy and accessible to everyone. As a team member, we’ll empower you to live this promise each day as you make a truly global impact in your career. We constantly strive for innovation, while transforming and improving the online shopping experience for our customers. We believe that individually we are incredible, but when we come together, our growth is infinite. In an industry that is constantly evolving, we are on a mission to make an impact on the global market, and the individual and collaborative efforts of our people are paramount to helping us succeed. Whether you work in one of our logistics centers, technology hubs, corporate offices or even from home, your role at iHerb will take you beyond what’s expected, turning challenge into change. If you re ready for it, we want you to join our team. Get started now. Job Description We are looking for a Senior Data Engineer to help evolve and scale our modern data ecosystem, including our data lake, data warehouse, and machine-learning enablement platforms. This role will contribute to the company’s data-driven culture, bring innovative approaches to cloud-native engineering, and help advance our MLOps capabilities to support production-grade AI/ML initiatives. You will collaborate closely with data scientists, analytics engineers, and cross-functional partners to deliver reliable, high-quality data and operationalized machine-learning 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. Qualifications 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. 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 #LI-JC1 Compensation : The expected salary range for this role is $116,000.00 - $170,000.00 USD. The actual base pay offered will be determined by factors such as the candidate's relevant experience, education, geographic location, and internal equity. If you like wild growth and working with happy, enthusiastic over-achievers, you ll enjoy your career with us! Staffing Agency Submission Notice iHerb does not accept unsolicited 3rd party ( Agency ) candidates. If you are an Agency, please send any requests to be considered as a supplier in our Vendor Management System to staffingvendors iherb.com. Do not contact iHerb employees directly. If requested to work on a role, any Agency candidates would be presented through the internal recruiting organization. iHerb is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. iHerb provides equal employment opportunities to all applicants for employment and prohibits discrimination and harassment.

Requirements

Must-haves

For A Better You
At iHerb, we believe that living a healthy and balanced life should be easy and accessible to everyone. As a team member, we’ll empower you to live this promise each day as you make a truly global impact in your career.
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.
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.

Nice-to-haves

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Keywords to mirror

aimlci/cdpythonsqlagiledockeraws

Platform: workday
Effort: long

Benefits

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