Snowflake Architect - DBT and Cortex AI

Cognizant is seeking a Data Engineering Lead / Data Architect with 15+ years of experience designing and delivering enterprise-scale, cloud-native data platforms across AWS, Azure, and GCP. This role owns the architecture, delivery, and governance of modern Lakehouse and cloud data warehouse platforms built on Snowflake and Databricks, and leads the build-out of AI-ready, GenAI-enabled analytics capabilities for enterprise clients. The ideal candidate combines deep hands-on dbt technical expertise with the ability to lead distributed engineering teams, mentor talent, and establish data engineering best practices across large-scale, multi-terabyte production environments.

• Enterprise Data Architecture: Architect and deliver enterprise-scale Snowflake Data Cloud and Databricks Lakehouse platforms supporting modern data warehousing, ELT, AI, and self-service analytics.

• Multi-Layer Platform Design: Design multi-layered data architectures (Raw, Curated, Business, Semantic) using Snowflake, dbt Cloud, and Snowpark Python.

• Ingestion & Change Data Capture: Implement Snowpipe, Streams & Tasks, Dynamic Tables, external stages, CDC frameworks, and SCD Type 1/Type 2 processing for batch and near real-time ingestion.

• ELT Framework Delivery: Build scalable ELT frameworks using dbt Cloud, Snowpark Python, and Snowflake SQL, incorporating reusable Jinja macros, incremental models, snapshots, automated testing, lineage, and documentation.

• CI/CD & Environment Management: Configure dbt Cloud jobs, deployment environments, GitHub-based CI/CD, and reusable dbt-utils packages to standardize enterprise transformations and automate production releases.

• Advanced Transformation Frameworks: Design and optimize Snowpark-based transformation frameworks using DataFrames, Python UDFs, stored procedures, and AI Functions.

• Performance Engineering: Optimize Snowflake performance through query profiling, micro-partitioning, clustering keys, materialization strategy, warehouse right-sizing, resource monitors, and auto-suspend/resume configuration.

• Governance & Security: Implement enterprise governance using RBAC, row- and column-level security, masking policies, Time Travel, Zero-Copy Cloning, secure data sharing, and external tables.

• AI-Ready & GenAI Platforms: Design AI-ready data platforms leveraging Snowflake Cortex, Cortex Analyst, and Cortex Search, along with semantic models and vector search, to enable GenAI-powered and conversational analytics.

• Cross-Platform AI/ML Integration: Integrate Snowflake with Databricks AI/ML ecosystems to enable advanced analytics, machine learning, semantic retrieval, and AI-powered business insights.

• Team Leadership: Lead enterprise modernization initiatives end-to-end — from architecture through production deployment — while mentoring engineering teams and establishing cloud data engineering best practices.

• 15+ years of experience designing and delivering enterprise-scale cloud-native data platforms across AWS, Azure, and GCP.

• Deep expertise in Snowflake, Snowpark, dbt Cloud, dbt Core, Databricks, Python, Spark (PySpark), and Informatica IICS.

• Proven track record building scalable ELT frameworks, modern cloud data warehouses, Lakehouse architectures, and AI-ready analytics platforms.

• Hands-on experience with Snowflake Cortex AI capabilities, semantic models, conversational analytics, and GenAI-powered analytics solutions built on Snowflake and Databricks.

• Experience implementing enterprise data modeling using dbt — staging, intermediate, marts, semantic layers, dimensional models, SCD Type 1/2 processing, data quality testing, lineage, and automated documentation.

• Strong background leading distributed engineering teams delivering secure, scalable, and cost-optimized cloud data platforms processing multi-terabyte enterprise workloads.

• Working knowledge of streaming and real-time processing (Kafka, Spark Structured Streaming) and orchestration tools (Dagster, Control-M, AutoSys, UC4, Tidal).

• Proficiency in Python, SQL, PL/SQL, and shell scripting, with DevOps/CI-CD experience using Terraform, GitHub, and Azure DevOps.

• Strong grounding in data governance practices including RBAC, data masking, data quality, and regulatory compliance.

• Data Architecture: Lakehouse, Enterprise Data Warehouse, Medallion Architecture, Semantic Data Layer.

• Snowflake Data Cloud: Snowpark, Snowpipe, Streams & Tasks, Time Travel, Zero-Copy Cloning, Secure Data Sharing, Dynamic Tables, Cortex Analyst, Cortex Search.

• Snowpark & Advanced Analytics: Snowpark Python, Snowpark ML, DataFrames, UDFs, Stored Procedures, AI Functions, ELT frameworks.

• AI / GenAI & Semantic Analytics: Semantic Models, Vector Search, LLM Integration, Conversational Analytics, RAG architectures, AI-ready data platforms.

• Databricks: Delta Lake, PySpark, Delta Live Tables (DLT).

• Data Engineering & Transformation: dbt Cloud, dbt Core, ELT design, incremental models, snapshots, seeds, sources, Jinja macros, data testing, documentation, CI/CD.

• Streaming & Real-Time Processing: Kafka, Spark Structured Streaming.

• Cloud Platforms: AWS, Azure, GCP.

• Programming: Python, SQL, PL/SQL, Shell Scripting.

• Orchestration: Dagster, Control-M, AutoSys, UC4, Tidal.

• DevOps & CI/CD: Terraform, GitHub, Azure DevOps.

• Data Governance: RBAC, Data Masking, Data Quality, Compliance.

• SnowPro Core Certification

• Databricks Certified Data Engineer Associate

• Informatica PowerCenter Developer Specialist

• AWS Certified Solutions Architect – Associate

We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role’s business requirements, this is a hybrid position requiring 2 days a week in a client or Cognizant office in Chicago, IL, state. Regardless of your working arrangement, we are here to support a healthy work-life balance though our various wellbeing programs.

The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.

Salary and Other Compensation:

Applications will be accepted until August 9, 2026.

The annual salary for this position is between $88,000 - $170,000 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

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 law.

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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