AWS Glue + Pyspark

Job Summary

Collaborate as a senior data engineer and data architect to design build and optimize scalable data solutions using PySpark and AWS Glue in a hybrid work model. Apply extensive experience to create robust data pipelines transform complex banking data and ensure high quality data for analytics and regulatory reporting while supporting retail banking initiatives and day shift operations.

Responsibilities

• Design and implement highly scalable data pipelines using PySpark and AWS Glue to process large volumes of structured and semi structured data with a strong focus on reliability and performance optimization

AWS Glue PySpark Lambda Step functions S3 Redshift Aurora Postgresql SQL Python Airflow CICD GIT

• Architect end to end data ingestion frameworks that integrate various source systems into centralized data platforms ensuring consistent data modeling and governance aligned with enterprise standards
• Optimize existing batch and near real time data workflows by analyzing bottlenecks and applying tuning techniques that improve execution time resource utilization and overall system stability
• Collaborate with business and analytics stakeholders to translate complex retail banking data requirements into clear technical specifications that guide robust data engineering and architecture solutions
• Develop reusable data transformation components and metadata driven frameworks that reduce duplication improve maintainability and support rapid onboarding of new data sources
• Implement comprehensive data quality checks and validation rules within pipelines to ensure accuracy completeness and timeliness of data used for reporting risk analysis and customer insights
• Document data models pipeline designs and operational procedures in a structured and accessible manner so cross functional teams can understand lineage dependencies and usage across the data ecosystem
• Coordinate with cloud infrastructure teams to define secure and cost efficient deployment patterns on AWS ensuring appropriate resource configuration monitoring alerting and backup strategies for data workloads
• Support data consumers by providing curated datasets and guidance on data usage enabling analytics teams to derive meaningful insights that improve customer experiences and operational efficiency in retail banking
• Troubleshoot production issues in data pipelines by performing root cause analysis implementing corrective actions and continuously refining monitoring to minimize business impact during day shift operations
• Mentor junior data engineers by sharing best practices in PySpark AWS Glue and data architecture helping build team capability and improving consistency across development and delivery activities
• Contribute to data governance initiatives by aligning data models classification rules and retention practices with regulatory requirements common in retail banking while also promoting ethical and responsible data use
• Evaluate emerging data engineering tools and architectural patterns recommending adoption where appropriate to enhance scalability resilience and innovation in the company data landscape

Qualifications

• Demonstrate deep practical expertise in PySpark and AWS Glue including building complex transformations handling large scale data and optimizing jobs for performance and reliability
• Bring strong experience in designing data architectures such as layered data models and canonical structures that support analytics reporting and regulatory needs in a financial services environment
• Apply working knowledge of retail banking concepts like accounts transactions and customer journeys to design data solutions that are aligned with domain specific requirements and business terminology
• Utilize broad proficiency with AWS cloud data services including storage compute and orchestration capabilities to deploy secure and well governed data engineering solutions
• Exhibit solid skills in SQL and data modeling to define schemas joins and aggregations that produce consistent and trustworthy datasets for downstream analytics and operational reporting
• Use experience with hybrid working models to collaborate effectively through onsite and remote interactions ensuring clear communication documentation and handoffs across global teams

Certifications Required

AWS Certified Data Analytics Specialty or AWS Certified Solutions Architect Associate recommended for PySpark and AWS Glue based data engineering

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。

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