Job Summary
Design and optimize robust data platforms as an Architect with extensive experience in Python Databricks SQL Databricks Workflows and PySpark working in a hybrid model during day shifts and focusing on scalable analytics solutions that power critical business decisions while improving data reliability performance and usability across the enterprise.
Responsibilities
- Define and maintain end to end data architecture blueprints that leverage Python Databricks SQL and PySpark to deliver scalable analytics solutions that directly support enterprise decision making and long term data strategy.
- Design optimized data models and lakehouse structures on Databricks that improve query performance reduce compute costs and enhance reliability for business intelligence and advanced analytics use cases.
- Develop robust data ingestion and transformation pipelines using PySpark and Databricks Workflows to ensure timely accurate and well structured data is available for downstream consumption by analysts and data scientists.
- Configure and orchestrate Databricks Workflows to automate complex data processes enforce dependencies and provide predictable repeatable execution patterns for critical data jobs.
- Collaborate with product owners and business stakeholders to translate analytical needs into practical technical architectures that align with organizational goals and deliver measurable business value.
- Implement strong data governance practices including data quality rules validation checks and monitoring frameworks to increase trust in enterprise data assets and reduce operational risk.
- Optimize Python and PySpark code for performance scalability and maintainability by applying best practices in modular design resource management and efficient data handling approaches.
- Guide teams on effective use of Databricks SQL for analytics reporting and interactive exploration ensuring queries are tuned and consistent with established data modeling standards.
- Establish and document architecture patterns standards and guidelines for hybrid work delivery enabling distributed teams to collaborate effectively while maintaining secure access to data platforms.
- Evaluate new features within Databricks and related data ecosystem tools to recommend improvements that enhance platform capabilities usability and alignment with future business needs.
- Partner with security and compliance teams to design architectures that protect sensitive data meet regulatory requirements and provide robust access control within the Databricks environment.
- Mentor less experienced data engineers and architects by sharing best practices in Python development PySpark optimization and Databricks platform usage to elevate overall team capability.
- Monitor production data pipelines and workflows to proactively identify performance bottlenecks capacity issues or failure patterns and implement sustainable technical remediation.
Qualifications
- Demonstrate extensive hands on experience designing and implementing data engineering solutions using Python with a strong focus on clean coding practices and reusable components.
- Show deep proficiency in Databricks SQL including writing complex analytical queries tuning execution plans and working with large scale structured and semi structured datasets.
- Apply advanced PySpark skills to build high volume data pipelines manage distributed processing and optimize transformations for both performance and reliability.
- Exhibit strong practical knowledge of Databricks Workflows including orchestration of multistep jobs scheduling strategies and integration with other platform services.
- Bring solid understanding of modern data architecture concepts such as lakehouse design batch and near real time processing and metadata management for enterprise scale implementations.
- Demonstrate experience working in hybrid work models that require effective remote and onsite collaboration while maintaining secure and efficient access to shared data environments.
- Display capacity to communicate complex technical designs in clear business oriented language enabling stakeholders to understand architectural tradeoffs and expected outcomes.
Salary and Other Compensation:
The annual salary for this position is between $90-115K depending on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
· Medical/Dental/Vision/Life Insurance
· Paid holidays plus Paid Time Off
· 401(k) plan and contributions
· Long-term/Short-term Disability
· Paid Parental Leave
· Employee Stock Purchase Plan
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable la
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。
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