Data Architect - Snowflake

We are seeking a highly skilled and hands-on Data Engineering Lead. In this role, you will lead the design, development, and optimization of enterprise-scale data platforms built on Snowflake.

Responsibilities Lead the design, development, and delivery of scalable Snowflake-based data platform solutions for enterprise clients Serve as the primary Snowflake technical lead within project teams, providing architectural guidance and establishing engineering best practices Design and implement Snowflake environments including account strategy, database and schema hierarchy, virtual warehouse sizing, and multi-cluster warehouse configurations Define and enforce medallion architecture (Bronze, Silver, Gold) aligned with business data domains and enterprise data strategies Design and implement batch, real-time, and CDC-based data ingestion frameworks from diverse source systems into Snowflake Integrate enterprise applications and source systems including SAP, Salesforce, MuleSoft, Boomi, REST APIs, event streams, and flat-file data sources Configure and manage Snowflake internal and external stages leveraging Azure Data Lake Storage, AWS S3, or Google Cloud Storage Implement Snowflake security and governance frameworks including RBAC, row access policies, column masking policies, and secure data sharing Lead environment management strategies using Time Travel and Zero-Copy Cloning for development, testing, and sandbox environments Develop and optimize data transformation frameworks using Snowflake SQL, Snowpark (Python), and modern ELT methodologies Leverage Snowflake Cortex AI capabilities and support AI/ML use cases through well-designed and governed data products Design and expose curated, business-ready datasets and semantic layers for BI and analytics tools such as Power BI, Tableau, and Sigma Drive performance tuning, workload optimization, monitoring, and cost management across Snowflake environments

Requirements 8+ years of hands‑on experience in Data Engineering, Data Warehousing, or Data Platform Engineering Minimum 3 years of hands‑on experience implementing and administering Snowflake in enterprise production environment Proven track record delivering at least two end‑to‑end Snowflake implementations from solution design through production go‑live Strong expertise in data modeling, data warehousing concepts, ELT/ETL design patterns, and cloud‑native data architecture Strong experience designing and implementing CDC‑based and event‑driven data processing solution Experience integrating Snowflake with ERP systems, preferably SAP (ODP, CDS Views, managed connectors), and/or iPaaS platforms such as Salesforce, MuleSoft, Boomi, or equivalent technologie Strong proficiency in SQL and Python, including Snowpark‑based development Good understanding of Snowflake Cortex AI/ML capabilities and their application within modern data platform Experience working with cloud storage platforms such as Azure Data Lake Storage, AWS S3, or Google Cloud Storage Experience handling structured and semi‑structured data formats including JSON, Parquet, and Avro Strong understanding of Snowflake security, governance, access controls, and compliance best practice Experience establishing BI connectivity and semantic data layers for tools such as Power BI, Tableau, or Sigma Experience with data transformation frameworks such as dbt, Informatica, or similar enterprise data integration tools is highly desirable Experience working within pharmaceutical, healthcare, life sciences, or other regulated industries is a plus Certifications Mandatory SnowPro Core Certification Preferred SnowPro Advanced: Data Engineer Certification #J-18808-Ljbffr.

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