Databricks Architect (Hybrid)

This hybrid role is for a Senior Architect with deep hands-on expertise in Databricks SQL, Databricks Workflows, and PySpark. The candidate will drive end-to-end data platform and analytics solutions for a global organization, designing, optimizing, and governing scalable cloud-based data architectures that enable trusted insights, improve business decisions, and support responsible innovation.

Key Responsibilities

• Design and implement scalable cloud-based data architectures on Databricks to support complex analytical workloads and reliable data products.
• Develop robust data pipelines using Databricks Workflows and PySpark to ingest, transform, and curate large volumes of structured and unstructured data.
• Optimize Databricks SQL queries and data models for high-performance, low-latency analytics.
• Establish reusable frameworks and patterns for data ingestion, transformation, and orchestration to improve consistency and reduce time-to-market.
• Collaborate with product owners, data engineers, and analysts to translate business requirements into scalable technical solutions aligned with enterprise architecture standards.
• Define and enforce best practices for code quality, version control, testing, and deployment in Databricks environments.
• Implement monitoring, logging, and alerting mechanisms to enhance reliability and observability of workflows and jobs.
• Design secure data access models (RBAC, data masking, encryption) to ensure compliance with regulatory and security standards.
• Drive modernization by migrating legacy systems into Databricks-based platforms, simplifying architecture and reducing operational complexity.
• Conduct architecture and code reviews to ensure performance, scalability, and maintainability.
• Partner with cloud and infrastructure teams to optimize compute, storage, and networking for cost efficiency and performance.
• Document architecture decisions, data flows, and standards for long-term maintainability.
• Mentor and guide teams on Databricks and PySpark implementation best practices.

• Extensive experience in designing and implementing enterprise-scale data platforms using Databricks, Databricks SQL, Databricks Workflows, and PySpark.
• Strong proficiency in SQL, data modeling, and query optimization for analytical workloads.
• Hands-on experience in building and orchestrating complex data pipelines within Databricks (scheduling, dependency management, recovery strategies).
• Solid understanding of distributed computing concepts (cluster configuration, parallel processing, resource tuning).
• Experience with data governance, data quality, and metadata management frameworks.
• Hands-on expertise with CI/CD practices for Databricks deployments, including automated testing and release pipelines.
• Strong communication and collaboration skills to work effectively with cross-functional stakeholders.
• Proven experience in large-scale cloud-based data ecosystems and modern data platform

• Databricks Certified Data Engineer Professional
• Databricks Certified Data Engineer Associate
• Equivalent cloud data certification (Azure, AWS, or GCP)
• Please note this role is not able to offer visa transfer or sponsorship now or in the future*

Salary And Other Compensation

Applications will be accepted until Aug 06, 2026,

The annual salary for this position is between $ 90,000 - $ 150,000 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