Data Integration Engineer II

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About mPulse: mPulse is the leader in conversational AI and member engagement for healthcare. We partner with the nation's most innovative health plans — Medicare Advantage, Medicaid, and Commercial — to engage members in conversations that close care gaps, improve Stars ratings, and drive better health outcomes. Our platform reaches tens of millions of Americans and is built on the belief that better health starts with better conversations. Job Summary We’re looking for a passionate, self-motivated, and detail-oriented individual to join our Data Operations team. In this role, you will design, develop, and maintain scalable data pipelines that power analytics, reporting, and product capabilities within the predict vertical of the organization. You will work closely with product engineering, implementation, analytics, and customer success teams to ensure data is accurate, reliable, and accessible. The ideal candidate has strong SQL and data warehousing experience, enjoys solving complex data problems, and is passionate about building reliable and well-documented data systems. Duties Responsibilities Design, develop, and maintain scalable data integration pipelines (ETL/ELT) to support data ingestion, transformation, cleansing, curation, and unification across multiple data sources. Develop and support end-to-end data pipeline components, including ingestion, validation, transformation, cleansing, and curated data layer development. Monitor, maintain, and optimize production data pipelines to ensure reliability, performance, and successful execution of scheduled workflows. Create and maintain comprehensive technical documentation for data pipelines, workflows, and data models. Develop tools and frameworks to support automated data profiling, data quality monitoring, and unit testing to ensure high-quality and reliable data assets. Collaborate with implementation teams to identify, investigate, and resolve data anomalies during data onboarding and integration processes. Partner with product engineering teams to ensure accurate data capture and alignment with application data specifications and business requirements. Provide data-related support to analytics and customer success teams, assisting with troubleshooting, reporting needs, and client data inquiries. Required Qualifications Bachelor’s or master’s degree in computer science, Engineering, or a related technical field, or equivalent practical experience. Minimum of 3 years of professional experience in data engineering, data integration, or a related role. Strong proficiency in SQL, including complex querying, data transformation, and query performance optimization. Experience working with cloud-based data platforms and services, particularly within AWS (e.g., S3, Secrets Manager/Vault, DMS, or similar services). Hands-on experience with modern data warehousing platforms, such as Snowflake, PostgreSQL, Amazon Redshift, or Microsoft SQL Server. Experience developing, debugging, and maintaining workflow orchestration pipelines using Apache Airflow, including DAG development and operational support. Experience using dbt (data build tool) to develop, test, and manage modular SQL-based data transformation models within modern data warehouse environments. Experience with version control systems and collaborative development workflows, using tools such as GitHub or Bitbucket. Proficiency in Python, particularly for data manipulation, automation, and integration tasks. Familiarity with CI/CD practices and automation tools, such as Jenkins or GitHub Actions. Strong written and verbal communication skills, with the ability to collaborate effectively across technical and non-technical teams. Preferred Qualifications Experience supporting data quality monitoring, data observability, or automated validation frameworks. Familiarity with data science or machine learning workflows from a data engineering perspective. Experience working with healthcare-related datasets, such as claims, cli

关于mPulse：mPulse是医疗保健领域对话式AI和会员互动领域的领导者。我们与美国最具创新性的健康计划——Medicare Advantage、Medicaid和Commercial——合作，让会员参与对话，从而弥合护理差距、提升Stars评级并推动更好的健康结果。我们的平台覆盖数千万美国人，其建立理念是更好的健康始于更好的对话。职位概述我们正在寻找一位充满热情、自我驱动且注重细节的人才加入我们的数据运营团队。在该职位中，您将设计、开发和维护可扩展的数据管道，为组织predict垂直领域内的分析、报告和产品能力提供支持。您将与产品工程、实施、分析和客户成功团队紧密合作，确保数据准确、可靠且可访问。理想的候选人应具备扎实的SQL和数据仓库经验，乐于解决复杂的数据问题，并热衷于构建可靠且文档完善的数据系统。职责设计、开发和维护可扩展的数据集成管道（ETL/ELT），以支持跨多个数据源的数据摄取、转换、清洗、整理和统一。开发并支持端到端数据管道组件，包括摄取、验证、转换、清洗和 curated 数据层开发。监控、维护和优化生产数据管道，以确保可靠性、性能和计划工作流的成功执行。为数据管道、工作流和数据模型创建并维护全面的技术文档。开发工具和框架，以支持自动化数据剖析、数据质量监控和单元测试，确保高质量且可靠的数据资产。与实施团队合作，在数据接入和集成过程中识别、调查并解决数据异常。与产品工程团队合作，确保准确的数据采集，并与应用数据规范和业务需求保持一致。为分析和客户成功团队提供数据相关支持，协助故障排除、报告需求和客户数据咨询。必备资格计算机科学、工程或相关技术领域的学士或硕士学位，或同等实践经验。至少3年数据工程、数据集成或相关角色的专业经验。精通SQL，包括复杂查询、数据转换和查询性能优化。具有使用基于云的数据平台和服务的经验，尤其是在AWS内（例如S3、Secrets Manager/Vault、DMS或类似服务）。具有现代数据仓库平台的实践经验，例如Snowflake、PostgreSQL、Amazon Redshift或Microsoft SQL Server。具有使用Apache Airflow开发、调试和维护工作流编排管道的经验，包括DAG开发和运营支持。具有使用dbt（data build tool）在现代数据仓库环境中开发、测试和管理基于SQL的模块化数据转换模型的经验。具有版本控制系统和协作开发工作流的经验，使用GitHub或Bitbucket等工具。精通Python，尤其是用于数据操作、自动化和集成任务。熟悉CI/CD实践和自动化工具，例如Jenkins或GitHub Actions。优秀的书面和口头沟通能力，能够跨技术和非技术团队有效协作。优先资格具有支持数据质量监控、数据可观测性或自动化验证框架的经验。从数据工程角度熟悉数据科学或机器学习工作流。具有处理医疗保健相关数据集的经验，例如claims、cli

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