Role: Snowflake Engineer Experience: 10 years + Experience in Snowflake, DBT, Airflow, Snowflake Cortex, SQL, Fivetran, Apache Airflow, OpenFlow, AWS Data Tools (Sage maker, Glue, Kinesis, Kafka, Microservices, Event Bridge) and Cloud Data Engineering.

Job Description

As a Snowflake Data Engineer, you will be the hands-on Snowflake Engineer for all Data engineering efforts. Your role is to design, implement, and optimize a high-performance data warehouse environment (Snowflake, Databricks, Astronomer, AWS) that serves as the "single source of truth." You won't just be moving data around; you'll be architecting a scalable foundation that enables real-time analytics, data sharing, and AI-driven insights across the enterprise. Your mission is to ensure that our technology data stack is scalable, secure, and—most importantly—aligned with where the company wants to be in five years. Key Responsibilities · Architecture Design: Design end-to-end data architectures within Snowflake, utilizing Virtual Warehouses, multi-cluster environments, and specialized storage optimization. · Data Modeling: Create and maintain complex data models (Star Schema, Snowflake Schema, Data Vault 2.0) tailored for cloud-native performance. Strong Collaboration with various IT and Business stakeholders to curate, document and implement Data lineage, knowledge graphs, Data tracing and Compliance KPIs. · Pipeline Engineering: Architect robust ETL/ELT pipelines using Snowpipe, Tasks, and Streams, integrating with tools like airbyte, dbt, Fivetran, or AWS. · Governance & Security: Implement Role-Based Access Control (RBAC), data masking, and row-level security to ensure compliance with global standards (GDPR, HIPAA, etc.). · Performance Tuning: Monitor and optimize query performance and credit consumption—ensuring the system is as cost-effective as it is fast. Expert-level SQL and deep knowledge of Snowflake-specific features (Time Travel, Zero-copy Cloning, Data Sharing etc. · Snowflake Reference Designs: Implement reference designs for ETL/ELT pipeline engineering including integration efforts with AWS platforms. · Data Governance: Setup architecture and governance standards from Data engineering and Platform perspective and enforce Data Lineage, Data Stewardship across all business units. Required Skills & Qualifications Category Requirements Experience 10+ years in IT, with 4-6 years in a formal progressive hands-on Development experience using Data Engineering in Snowflake and AWS, Semantic Engineering, Data Modelling, Data Governance role. Technical Breadth Mastery of cloud ecosystems (AWS), microservices, Snowflake, Snowflake OpenFlow, Snowflake Cortex, Data standards, IT Governance, Security Architecture, Architecture Guidance, Security and data integration patterns. 7+ years in Data Engineering, with 3+ years focused specifically on Snowflake implementations. Frameworks Proficiency in Data Platforms – Snowflake, Microsoft SQL, Postgres, Databricks, Microsoft Power BI, Tableau Reporting, AWS Data (Sage maker, Glue, Kinesis, RDS), Python, JavaScript, Scala, Go and Java programming. Soft Skills High-level "influencing without authority" and the ability to explain complex data engineering tech to non-techies. Certifications Snowflake Core Pro and Snowflake Advanced Architect Certifications(mandatory), AWS Data Engineering Certifications (nice to have), AWS Solution Architect Pro (nice to have) Education Bachelor’s or Master’s in CS, MIS, Data Science, MBA or a related field (or equivalent battle-hardened experience). Duration: 3-5 Months (extensible based on performance)