Data Engineering Lead / Data Architect

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 Google Cloud Platform. 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., * 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, Google Cloud Platform.
• 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.

15+ years in enterprise data engineering and architecture, * 15+ years of experience designing and delivering enterprise-scale cloud-native data platforms across AWS, Azure, and Google Cloud Platform.

• 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.