Senior Data Engineer

🕒 Julho 10

🏄 California – Remoto

🏄 California – Remote

💵 $116.000 - $170.000 / ano

⏰ Tempo Integral

🟠 Sênior

🚰 Engenheiro de Dados

👻 Score fantasma 10%

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iHerb

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

🛍️ Comércio Eletrônico

🛒 Varejo

🧘 Bem-estar

eCommerce • Retail • Wellness

A iHerb é uma varejista online fundada em 1996, que oferece uma seleção cuidadosa de produtos de saúde e bem-estar para consumidores em todo o mundo. A empresa foca em vitaminas, suplementos e remédios naturais, nutrição esportiva, alimentos naturais e secos, além de produtos ecologicamente corretos, com a missão de tornar a saúde e o bem-estar acessíveis, acessíveis e convenientes. A iHerb opera como um negócio de comércio eletrônico com foco no varejo e enfatiza a experiência do cliente e uma cultura empresarial orientada por valores.

Descrição

• 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.

🎯 Requisitos

• 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

🏖️ Benefícios

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