1. Responsible for data cleaning (ETL) and data warehouse construction to support large-scale AI models. 2. Responsible for training and fine-tuning large AI models to meet the requirements of specific business scenarios. 3. Responsible for developing supporting tools, such as dashboards and general business logic, to ensure the practicality of AI model applications. 4. Must have hands-on development experience and be able to lead a team or independently complete projects related to data collection and development.

• Define partner onboarding and clean room architecture patterns across Snowflake, LiveRamp, and Databricks that are secure, scalable, and repeatable • Configure and manage partner-specific clean room environments; deploy and manage Python-based libraries within the platform ecosystem • Establish and maintain MLOps practices, including model serving, monitoring, and pipeline orchestration for AI/ML features deployed within the platform ecosystem • Own design and enforcement of granular RBAC policies and least-privilege service accounts • Serve as the technical lead for onboarding new partners, implementing privacy-preserving controls (e.g., aggregation thresholds and anonymization techniques) • Design, build, and operate scalable ELT pipelines using Snowpark and/or PySpark and advanced SQL to provision Gold datasets • Implement and evolve identity resolution logic mapping internal data to 3P identifiers (including LUIDs, RampIDs, TransUnion IDs), ensuring privacy-safe practices • Design and operate scalable data architectures across Snowflake and Databricks supporting batch and near real-time processing patterns • Build robust automated checks (e.g., Great Expectations or custom SQL assertions) and define quality standards to detect schema drift, null rate spikes, and volume anomalies • Lead performance optimization across platforms (query tuning, caching, incremental processing) and define and implement query tagging and chargeback models for accurate cost attribution • Establish monitoring, alerting, runbooks, and standard operating procedures to improve platform reliability and reduce incident time-to-resolution • Validate that output data adheres to privacy and business requirements, and define test strategies for partner-facing releases • Serve as the escalation point for diagnosing connection failures, data discrepancies, or latency issues with partner technical teams • Design and build internal AI agents (using frameworks like LangChain, Snowflake Cortex) and mentor other engineers through code reviews, design discussions, and operational best practices

• Title: Senior Data Engineer • Location: Remote • Duration: C2H • Salary: 1-2Lkhs Per Month

• Lead teams of software engineers to improve the lives of Henry Schein One users and their patients through the creation of market-leading solutions • Help recruit, develop, and retain engineers to create a high-performing team • Oversee team leaders and support them in fulfilling their mission and delivery commitments • Enable engineering teams to practice continuous delivery at scale by identifying and removing workflow roadblocks • Inspire, motivate and coach to enable continuous development of team leaders and their teams • Ensure products are secure, compliant and reliable for Henry Schein One customers • Collaborate with senior leaders on the vision and strategy for workstreams and products • Partner closely with Product leadership to influence and understand the strategic commitments of your teams • Build strong cross-functional relationships and influence others to pursue the best possible user experience • Enable process and practice maturity to deliver on global growth opportunities • Positively and collaboratively influence decision-making to improve practices across your teams, department, and stakeholder network • Travel typically up to 10% of time • Office environment with no special physical demands required