Sr. Data Engineer I

iHerb, LLC • Home Gardens, CA

6d • $116,000 - $170,000

About The Position

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.

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.

Nice To Haves

• People person, team player with a strong can-do mentality
• 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

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.

Benefits

• The actual base pay offered will be determined by factors such as the candidate's relevant experience, education, geographic location, and internal equity.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior