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
About Cognizant
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Things to know before you apply
- Work authorization: Cognizant will only consider applicants for this position who are legally authorized to work in Canada without requiring employer sponsorship, now or at any time in the future.
- Accommodations: If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email [email protected] with your request and contact information.
- AI in hiring: We use artificial intelligence (AI) tools to help us screen and assess applications more efficiently. Our team performs additional human review and ultimately decides who moves forward in our hiring process.
- Open position: Unless otherwise stated, this job posting is for an open position on our team.
- Language proficiency: We ask that all candidates have basic English proficiency for company-wide communications purposes. For roles based in Quebec, professional English proficiency is required, as you’ll deliver services to and collaborate with stakeholders outside the province who may not speak French.
- Inclusion: Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, provincial or local laws.
