Sr. Data Engineer I
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Requirements
• 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 • Hired applicant may be awarded Restrict Stock Units and receive annual bonuses pursuant to eligibility and performance criteria defined in the respective plan documents and policies. • Anticipated Pay Scale: • $115,850 - $184,307 USD • 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 [email protected]. Do not contact iHerb employees directly. If requested to work on a role, any Agency candidates would be presented through the internal recruiting organization. • Staffing Agency Submission Notice • Focus on the Customer · Empower Our People · Be Entrepreneurial & Pivot Quickly · Embrace Diversity & Inclusion · Strive for Simplicity • Focus on the Customer · Empower Our People · Be Entrepreneurial & Pivot Quickly · • Embrace Diversity & Inclusion · Strive for Simplicity • iHerb BenefitsAt iHerb, we are dedicated to offering programs designed to help our employees and their families stay healthy, live well, and plan for their financial future. Built on a strong foundation, our programs provide options and upgrades with flexibility, protection, and security in mind. For the comprehensive benefits list, visit www.iHerbBenefits.com. For our international team members, you may be eligible for benefits depending on the country where you are employed. The Talent Acquisition Partner/local HR representative will go over the benefits you are eligible for.
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.
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