Job Summary
Senior Architect role in a multinational organization focused on building scalable data and analytics solutions using Python LakeHouse platforms Spark Job Definition OneLake SQL and PySpark. The role requires deep expertise in retail banking data and processes delivering secure and performant hybrid cloud data architectures that drive business value in a hybrid work model with day shift.
Responsibilities
Design robust LakeHouse based data architectures that integrate OneLake Spark Job Definition and PySpark components to support large scale retail banking analytics and reporting use casesDevelop end to end data pipelines using Python SQL and PySpark that efficiently ingest transform and curate transactional and customer data from multiple retail banking source systemsOptimize Spark Job Definition configurations and job orchestration to achieve predictable performance cost efficiency and high reliability for critical banking workloadsImplement secure data models and access patterns in LakeHouse and OneLake environments that align with retail banking regulatory requirements and internal risk policiesCollaborate with product owners and domain experts in retail banking to translate business goals into technical data architecture blueprints and implementation roadmapsGuide engineering teams on best practices for coding standards performance tuning and reusable patterns in Python SQL and PySpark across hybrid environmentsReview solution designs technical specifications and implementation plans to ensure alignment with enterprise architecture standards and long term data strategyCoordinate with cloud infrastructure and security teams to ensure LakeHouse and OneLake solutions meet resilience observability and compliance expectationsCreate detailed documentation for data models data flows pipeline configurations and operational runbooks to support sustainable day to day operations by delivery teamsMentor less experienced team members by providing constructive feedback technical coaching and examples of effective patterns for data engineering in banking contextsEngage with stakeholders to evaluate new data platform features and tools assessing their suitability for retail banking use cases and recommending adoption pathsDrive continuous improvement initiatives by analyzing production issues identifying root causes and implementing architectural enhancements that reduce risk and improve stabilityAlign data solution outcomes with organizational goals by demonstrating how improved data quality timeliness and accessibility enable better customer experiences in retail banking
Qualifications
Require extensive experience designing and implementing LakeHouse based data platforms with OneLake or comparable technologies in complex enterprise environmentsRequire strong hands on expertise in Python SQL and PySpark for building scalable data pipelines reusable libraries and automation frameworks supporting analytics and reportingRequire deep domain knowledge of retail banking including deposits loans cards customer journeys and regulatory reporting to ensure solutions meet functional expectationsRequire proven experience working with Spark Job Definition including job configuration resource tuning monitoring and optimization for both batch and near real time workloadsRequire experience in hybrid work environments collaborating across global teams using structured documentation version control and standard agile practicesNice to have experience designing solutions that integrate LakeHouse platforms with downstream analytics tools dashboards and machine learning workflows in banking scenariosNice to have exposure to data governance practices such as data cataloging lineage tracking and quality frameworks that enhance trust in banking data productsNice to have familiarity with performance engineering techniques on large relational datasets using advanced SQL optimization and partitioning strategies
Certifications Required
Preferred certifications include Microsoft Azure Data Engineer Associate or Databricks Data Engineer Professional or equivalent data engineering certifications
Über Cognizant
Cognizant (NASDAQ: CTSH) i ist ein Technologiedienstleister und Entwickler von KI-Lösungen. Wir schlagen die Brücke zwischen KI-Investitionen und echtem unternehmerischem Mehrwert, indem wir ganzheitliche Full-Stack-KI-Lösungen für unsere Kunden entwickeln. Mit unserer fundierten Branchen-, Prozess- und Engineering-Expertise integrieren wir die spezifischen Anforderungen von Unternehmen passgenau in Technologiesysteme. So entfalten wir das menschliche Potenzial, erzielen greifbare Ergebnisse und sichern globalen Unternehmen in einer sich rasant wandelnden Welt den entscheidenden Vorsprung. Erfahren Sie mehr unter cognizant.ai oder @cognizant.
Zusätzliche Informationen zur Beschäftigung
Die Vergütungsinformationen sind zum Zeitpunkt der Veröffentlichung dieser Stellenausschreibung korrekt. Cognizant behält sich das Recht vor, diese Informationen jederzeit unter Beachtung der geltenden gesetzlichen Bestimmungen zu ändern.
Bewerberinnen und Bewerber können verpflichtet sein, an Vorstellungsgesprächen persönlich oder per Videokonferenz teilzunehmen. Darüber hinaus kann es erforderlich sein, bei jedem Gespräch einen gültigen staatlichen Lichtbildausweis vorzulegen.
Cognizant ist ein Arbeitgeber mit Chancengleichheit. Ihre Bewerbung und Kandidatur werden nicht aufgrund von Rasse, Hautfarbe, Geschlecht, Religion, Glaubensbekenntnis, sexueller Orientierung, Geschlechtsidentität, nationaler Herkunft, Behinderung, genetischen Informationen, Schwangerschaft, Veteranenstatus oder sonstiger durch bundes‑, landes‑ oder kommunalrechtliche Vorschriften geschützter Merkmale berücksichtigt oder abgelehnt.
