Job Summary
Serve as a senior technical expert acting as a lead developer in data engineering initiatives that use SQL Azure Databricks Azure Data Lake Store and Azure Data Factory to build scalable data solutions in a hybrid work model. Contribute to high quality data platforms that support investment banking focused analytics and operational reporting in a collaborative day shift environment.
Responsibilities
Design robust data engineering solutions using SQL Azure Databricks Azure Data Lake Store and Azure Data Factory to deliver secure and scalable data pipelines for complex analytics needs in investment banking contextsDevelop efficient batch and near real time data pipelines that ingest transform and curate structured and semi structured data to enable high quality downstream reporting and regulatory analysisImplement optimized SQL queries stored procedures and data models that improve query performance reduce compute cost and ensure reliable data access for analytical users and operational teamsConfigure and maintain Azure Databricks clusters notebooks and jobs to orchestrate data transformations while managing resource utilization and promoting consistent engineering practicesBuild and manage Azure Data Factory pipelines triggers and activities to automate data movement across diverse sources and sinks while enforcing standardized patterns and reusable componentsAdminister Azure Data Lake Store structures access patterns and data lifecycle policies to ensure organized storage cost effectiveness and secure handling of sensitive financial informationCoordinate with business analysts and stakeholders from investment banking operations to translate requirements into precise technical specifications and data transformations that align with business objectivesCollaborate with quality engineering and operations teams to design validation checks reconciliation controls and monitoring processes that ensure data completeness accuracy and timelinessGuide peers on best practices in coding testing documentation and version control to maintain high engineering standards and consistent delivery of maintainable data solutionsPerform root cause analysis for data issues production incidents and performance bottlenecks then implement durable fixes and preventive improvements that enhance platform stabilityDocument solution designs data flow diagrams data dictionaries and operational runbooks in clear language so that cross functional teams can easily understand maintain and extend the platformsEngage with architecture and security teams to align implementations with enterprise reference patterns compliance expectations and resiliency objectives across the Azure data platformParticipate in sprint planning backlog refinement and technical reviews to ensure that stories are scoped realistically risks are identified early and deliverables meet quality expectations for stakeholders
Qualifications
Combine extensive hands on experience in SQL Azure Databricks Azure Data Lake Store and Azure Data Factory with an ability to design end to end data pipelines that support analytics for financial servicesApply knowledge of investment banking operations or investment banking and brokerage processes when available to shape data models transformation rules and controls that reflect real world trade and post trade workflowsDemonstrate strong understanding of data warehousing concepts dimensional modeling and performance tuning methods that support high volume and low latency analytical workloadsUtilize experience with hybrid working environments and cross functional collaboration tools to maintain clear communication effective documentation and timely delivery across distributed teamsExhibit solid problem solving skills attention to detail and a continuous improvement mindset that support reliable production operations and incremental enhancement of the data platform
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
薪酬信息截至本职位发布之日为准。Cognizant 保留在适用法律允许的范围内随时修改该信息的权利。
申请人可能需要通过现场面试或视频会议的方式参加面试。此外，候选人在每次面试时可能需要出示其当前所在州或政府签发的有效身份证件。
Cognizant 是一家提供平等就业机会的雇主。在招聘过程中，您的申请和候选资格不会因种族、肤色、性别、宗教、信仰、性取向、性别认同、国籍、残疾、遗传信息、怀孕、退伍军人身份或任何其他受联邦、州或地方法律保护的特征而受到影响。
