We are looking for a Architect Data Engineer (Snowflake+DBT+Cortex AI) Locations: |#Bangalore | #Chennai

Experience:
12+ Years Employment Type: Full time | Hybrid (3 days work from office) Interview Mode: 1 Round – Face-to-Face (Mandatory) Notice Period: Immediate Joiners Apply Now: https://lnkd.in/eEdAXStA Hands-on Exp: Snowflake 7+yrs Hands-on Exp: DBT:3+yrs Hands-on Exp: Cortex AI: 6months+ Role Summary We are looking for a Snowflake Architect to design and lead enterprise-scale cloud data platforms for our clients. You will define target-state architectures, set modeling, transformation, and governance standards, and guide delivery teams from solution design through production rollout. A key part of the role is shaping AI-ready data platforms, using Snowflake’s AI capabilities and a well-governed dbt-based transformation layer. This is a client-facing role that also involves mentoring engineers.

Key Responsibilities
Architecture and Design
• Design end-to-end Snowflake solutions covering ingestion, storage, transformation, consumption, and data sharing.
• Define reference architectures, patterns, and standards for Snowflake on AWS.
• Create conceptual, logical, and physical data models using dimensional, Data Vault, and normalized approaches, as appropriate for each use case.
• Lead platform decisions on warehouse sizing, multi-cluster strategy, database and schema design, clustering, and cost optimization.
• Architect secure, scalable integrations with AWS services such as S3, IAM, Glue, Lambda etc. dbt and Transformation Architecture
• Define the dbt project architecture: layering (staging, intermediate, marts), naming conventions, modularity, and reuse through packages and macros.
• Establish testing, documentation, and data contract practices in dbt, including source freshness checks, schema tests,



and unit tests.
• Design CI/CD for dbt (slim CI, state-based deploys, environment promotion) using Git-based workflows and orchestrators such as Airflow, dbt Cloud, or Snowflake Tasks.
• Optimize dbt models on Snowflake: incremental strategies, materialization choices, and warehouse usage. AI and Advanced Analytics
• Architect AI-ready data foundations: curated, governed, and well-documented data that supports ML and GenAI use cases.
• Design solutions using Snowflake AI capabilities such as Cortex (LLM functions, Cortex Search, Cortex Analyst), Snowpark, Snowpark ML, and the Snowflake Model Registry.
• Ensure AI workloads respect access controls, data privacy, cost guardrails, and responsible AI practices.
• Evaluate and advise clients on build-vs-buy choices and feature maturity for AI capabilities. Data Governance and Security
• Establish governance frameworks covering data classification, lineage, cataloging, quality, and stewardship.
• Implement role-based access control, row-level security, dynamic data masking, tagging, and object-level policies in Snowflake.
• Extend governance to AI use cases, including controlled access to sensitive data for LLM and model workloads, plus auditability.
• Define data retention, auditing, and monitoring practices. Engineering Leadership
• Guide teams on advanced SQL, Snowpipe, Streams and Tasks, Dynamic Tables, stored procedures, and performance tuning.




• Define DevOps practices for Snowflake (e.g., Terraform, schemachange, Git-based workflows).
• Review designs and code, and enforce engineering best practices.
• Mentor engineers and build capability across the practice. Client and Pre-Sales Engagement
• Run architecture workshops and discovery sessions with client stakeholders.
• Support solutioning, estimation, and technical responses for RFPs and SOWs.
• Present architecture decisions, trade-offs, and roadmaps to senior stakeholders. Required Skills
• Snowflake: Deep expertise in architecture, performance optimization, security, data sharing, and cost management.
• Data Architecture: Strong experience designing data warehouses, lakehouses, and modern data platforms.
• Architecture Modeling: Proficiency in conceptual, logical, and physical modeling (Kimball, Inmon, Data Vault 2.0) using tools such as ER/Studio, ERwin, or equivalent.
• Data Governance: Hands-on experience with governance, metadata management, lineage, data quality, and access control.
• SQL: Expert-level SQL, including complex transformations, query tuning, and analytical functions.
• AWS: Solid working knowledge of the AWS data ecosystem, including S3, IAM, Glue, Lambda, and networking and security fundamentals.
• dbt: Hands-on experience architecting and scaling dbt projects (Core or Cloud) on Snowflake, including testing, macros, and CI/CD.
• AI: Working knowledge of Snowflake Cortex and Snowpark, with practical experience supporting ML or GenAI use cases on enterprise data platforms. Certifications
• SnowPro Advanced: Architect certification (or SnowPro Core with a plan to certify)
• AWS certification (Solutions Architect or Data Analytics specialty)

📌 Architect Data Engineer (Bengaluru)
🏢 Perficient India
📍 Bengaluru