Job Summary
Serve as an architect for data and analytics solutions built on Databricks platforms designing scalable pipelines and frameworks that leverage Databricks SQL Databricks Workflows and PySpark. Apply extensive experience to optimize hybrid day shift delivery enable reliable insights for stakeholders and support innovation in insurance focused business environments.
Responsibilities
Design robust data architecture on Databricks platforms that aligns with enterprise standards and enables secure scalable analytics across hybrid work environmentsDevelop end to end data pipelines using PySpark and Databricks Workflows that transform complex raw data into curated datasets ready for business consumptionOptimize Databricks SQL queries and data models to improve performance reduce compute cost and ensure consistent response times for analytical workloadsCreate reusable frameworks and patterns for ingestion transformation and data quality that can be adopted by engineering teams across multiple initiativesCollaborate with product and business stakeholders to translate analytical and reporting needs into well defined data architecture and implementation plansIntegrate data from diverse source systems into unified models that support actuarial analysis financial reporting and operational monitoring for insurance focused solutionsDefine standards for coding testing and documentation of PySpark jobs and Databricks assets to promote maintainability and long term platform resilienceImplement monitoring and alerting strategies on Databricks Workflows to ensure timely detection of pipeline failures and to support reliable data delivery in day shift operationsPartner with security and compliance teams to embed data governance privacy controls and audit readiness into all Databricks based solutionsGuide teams on efficient use of Databricks clusters storage and caching options to balance performance objectives with infrastructure cost managementDocument architectural decisions data flows and dependency maps so that teams can easily understand solution design and support future enhancementsCoordinate with cross functional teams in a hybrid work model to plan releases manage dependencies and ensure smooth deployment of new data capabilities
Qualifications
Apply extensive experience in Databricks SQL to design analytical models reporting layers and interactive queries that support complex business insightsLeverage advanced proficiency in Databricks Workflows to orchestrate jobs manage dependencies and automate data operations with strong reliabilityUtilize deep PySpark expertise to implement scalable transformations handle large data volumes and enforce data quality rules across the pipeline lifecycleDraw on knowledge of life and annuities insurance to shape data models metrics and validations that reflect key policy claim and risk concepts when requiredDemonstrate proven ability gained from twelve to sixteen years of experience in data engineering and architecture to handle complex enterprise grade solutionsApply strong communication and collaboration capabilities to work effectively with business and technology partners in a hybrid non travel environmentUse experience with modern data platforms and cloud ecosystems to integrate Databricks solutions into broader enterprise architectures for long term value
Certifications Required
Preferred certifications include Databricks Certified Data Engineer Professional or Databricks Certified Data Engineer Associate.
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
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