Job description

Job Summary Drive strategic data modeling solutions for complex analytical and operational platforms spanning DataLake cloud ecosystems and advanced LLM GenAI integration architecture. Apply extensive experience in Azure AWS Snowflake and Databricks to design scalable data assets that power decision intelligence. Collaborate across teams using a hybrid work model and support day shift delivery in a global MNC environment. Responsibilities • Design and maintain enterprise data models that align business objectives with robust logical and physical structures across DataLake and multiple cloud platforms to ensure consistent and reliable data usage across the organization. • Develop optimized data structures and patterns in Snowflake and Databricks that improve query performance reduce compute cost and enable flexible analytics for diverse internal and external stakeholders. • Define and implement data modeling standards and best practices that govern naming conventions metadata management and versioning to foster long term maintainability and reduce technical debt. • Collaborate closely with data engineers and solution architects to translate complex business requirements into scalable data models that support ingestion transformation and consumption in Azure and AWS environments. • Architect integration patterns between DataLake operational data stores and LLM GenAI platforms to enable secure and governed access to curated data assets for advanced machine learning and generative workloads. • Map source systems to target data models and document lineage in detail so that downstream consumers can trace data origin understand transformations and comply with regulatory and audit expectations. • Conduct thorough impact assessments and change analysis for proposed enhancements to data structures to minimize disruption maintain backward compatibility and ensure stable performance for critical applications. • Review and refine data partitioning clustering and indexing strategies within Snowflake and Databricks environments to improve scalability for large datasets and reduce latency for analytical queries. • Collaborate with governance and security teams to embed data quality checks access controls and compliance policies into data modeling designs that safeguard sensitive information and respect privacy regulations. • Provide technical guidance to project teams on data modeling concepts including normalization dimensional modeling and schema evolution to ensure consistency across multiple programs and regions. • Engage with business partners to capture conceptual data requirements in clear models that bridge domain understanding and technology implementation especially for scenarios that benefit from LLM and GenAI solutions. • Evaluate new cloud data platform capabilities and emerging modeling techniques to continuously improve data architecture decisions and enhance the organizations ability to deliver innovative digital solutions. • Document data models integration patterns and metadata in accessible repositories so that teams can reuse assets reduce duplication of effort and accelerate delivery of new analytical products. Qualifications • Apply extensive experience in DataLake design to create resilient ingestion zones curated layers and consumption views that support both batch and near real time data access. • Leverage strong Azure and AWS cloud skills to integrate storage compute and security services into data models that operate efficiently across hybrid and multi cloud environments. • Utilize deep Snowflake expertise to configure schemas virtual warehouses and resource monitors that align with performance and cost optimization targets for enterprise analytics. • Employ advanced Databricks knowledge to design notebooks delta tables and lakehouse patterns that unify data engineering and machine learning workflows on shared models. • Use practical experience in LLM GenAI platform integration architecture to structure prompt ready datasets embeddings and knowledge stores that improve quality and safety of generative applications. • Draw on property and casualty insurance domain exposure when available to model policy claims risk and exposure data in ways that support underwriting pricing and regulatory reporting teams. Certifications Required Preferred certifications include Azure Data Engineer Associate AWS Certified Data Analytics Specialty Snowflake SnowPro Core and Databricks Data Engineer Associate. 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. Additional employment information Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview. 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, state or local laws. If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email CareersNA2@cognizant.com for roles based in the Americas or CareersIndia2@cognizant.com for roles based in India.

Requirements

Must-haves

Develop optimized data structures and patterns in Snowflake and Databricks that improve query performance reduce compute cost and enable flexible analytics for diverse internal and external stakeholders.
Define and implement data modeling standards and best practices that govern naming conventions metadata management and versioning to foster long term maintainability and reduce technical debt.
Collaborate closely with data engineers and solution architects to translate complex business requirements into scalable data models that support ingestion transformation and consumption in Azure and AWS environments.
Architect integration patterns between DataLake operational data stores and LLM GenAI platforms to enable secure and governed access to curated data assets for advanced machine learning and generative workloads.
Map source systems to target data models and document lineage in detail so that downstream consumers can trace data origin understand transformations and comply with regulatory and audit expectations.
Conduct thorough impact assessments and change analysis for proposed enhancements to data structures to minimize disruption maintain backward compatibility and ensure stable performance for critical applications.
Review and refine data partitioning clustering and indexing strategies within Snowflake and Databricks environments to improve scalability for large datasets and reduce latency for analytical queries.
Collaborate with governance and security teams to embed data quality checks access controls and compliance policies into data modeling designs that safeguard sensitive information and respect privacy regulations.
Provide technical guidance to project teams on data modeling concepts including normalization dimensional modeling and schema evolution to ensure consistency across multiple programs and regions.
Engage with business partners to capture conceptual data requirements in clear models that bridge domain understanding and technology implementation especially for scenarios that benefit from LLM and GenAI solutions.
Evaluate new cloud data platform capabilities and emerging modeling techniques to continuously improve data architecture decisions and enhance the organizations ability to deliver innovative digital solutions.
Document data models integration patterns and metadata in accessible repositories so that teams can reuse assets reduce duplication of effort and accelerate delivery of new analytical products.
Apply extensive experience in DataLake design to create resilient ingestion zones curated layers and consumption views that support both batch and near real time data access.
Leverage strong Azure and AWS cloud skills to integrate storage compute and security services into data models that operate efficiently across hybrid and multi cloud environments.
Utilize deep Snowflake expertise to configure schemas virtual warehouses and resource monitors that align with performance and cost optimization targets for enterprise analytics.
Employ advanced Databricks knowledge to design notebooks delta tables and lakehouse patterns that unify data engineering and machine learning workflows on shared models.
Use practical experience in LLM GenAI platform integration architecture to structure prompt ready datasets embeddings and knowledge stores that improve quality and safety of generative applications.
Draw on property and casualty insurance domain exposure when available to model policy claims risk and exposure data in ways that support underwriting pricing and regulatory reporting teams.
Certifications Required

Nice-to-haves

Preferred certifications include Azure Data Engineer Associate AWS Certified Data Analytics Specialty Snowflake SnowPro Core and Databricks Data Engineer Associate.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

llmazureawsmachine learningai

Platform: cognizant
Effort: medium