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.
コグニザントについて
コグニザント（NASDAQ: CTSH）は、AI Builderおよびテクノロジーサービスプロバイダーとして、お客様にフルスタックのAIソリューションを構築することで、AI投資と企業価値を結ぶ架け橋となっています。業界、ビジネスプロセス、エンジニアリングに関する当社の深い専門知識を活かし、組織固有のビジネス環境をテクノロジー・システムに組み込みます。これにより、人間の可能性を最大限に引き出し、確かな成果を実現するとともに、急速に変化する世界においてグローバル企業が常に一歩先を行くための支援を行っています。 詳細については、cognizant.ai をご覧ください。
雇用に関する追加情報
本募集に記載されている報酬情報は、掲載日時点で正確なものです。Cognizantは、適用される法令に従い、いつでも本情報を変更する権利を留保します。
応募者は、対面またはビデオ会議による面接への参加を求められる場合があります。また、各面接の際に、現在有効な州政府または政府発行の身分証明書の提示を求められる場合があります。
Cognizantは機会均等雇用主です。応募および選考において、人種、肌の色、性別、宗教、信条、性的指向、性自認、国籍、障がい、遺伝情報、妊娠、退役軍人の地位、その他連邦法・州法・地方自治体の法律により保護されるいかなる特性に基づく差別も行いません。
