Senior Data Engineer

🕒 il y a 27 jours

🏄 California – Distant

🏄 California – Remote

💵 $116 000 - $170 000 / an

⏰ Temps Plein

🟠 Senior

🚰 Ingénieur Data

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iHerb

Site WebLinkedInTous les Emplois

1001 - 5000 employés

🛍️ eCommerce

🛒 Commerce de détail

🧘 Bien-être

eCommerce • Retail • Wellness

iHerb est un détaillant en ligne fondé en 1996, qui offre une sélection de produits de santé et de bien-être soigneusement choisie aux consommateurs du monde entier. L'entreprise se concentre sur les vitamines, les compléments alimentaires et remèdes naturels, la nutrition sportive, les aliments naturels et secs, ainsi que sur les produits respectueux de l'environnement. Sa mission est de rendre la santé et le bien-être accessibles, abordables et pratiques. iHerb opère en tant qu'entreprise de commerce électronique avec un focus sur le retail, mettant l'accent sur l'expérience client et une culture d'entreprise axée sur les valeurs.

Description

• 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.

🎯 Exigences

• 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. • 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

🏖️ Avantages

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Remote work options