How much salary can I expect as a Snowflake Data Architect in Infosys in Bengaluru/Bangalore?

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Snowflake Data Architect

Infosys

Snowflake Data Architect

Infosys

Bengaluru/Bangalore

Not disclosed

Work from Office

Full Time

Min. 5 years

Job Description

Snowflake Architect

• Primary skills:Technology->Data Management->Data Architecture,Technology->Data on Cloud-DataStore->Snowflake,Technology->Oracle Industry Solutions->Oracle Clinical->Oracle LSH [Life Sciences Data Hub] • Knowledge of architectural design patterns, performance tuning, database and functional designs • Hands-on experience in Service Oriented Architecture • Ability to lead solution development and delivery for the design solutions • Experience in designing high level and low level documents is a plus • Good understanding of SDLC is a pre-requisite • Awareness of latest technologies and trends • Logical thinking and problem solving skills along with an ability to collaborate Required Skills & Experience: • Extensive work experience in Snowflake • Strong understanding of data lake architecture patterns and modern data platform design. • Proven experience in architecting and implementing object store-based data lakes using Apache Iceberg. • Experience with performance optimization and benchmarking • Experienced in setting up various services and conversant with data governance aspects • Familiarity with DevOps practices, CI/CD pipelines, and infrastructure-as-code. • Excellent communication and leadership skills to engage with clients and lead technical teams. Key Responsibilities: • Lead architectural solutioning and technical discussions with clients, focusing on snowflake-based data platforms. • Design and implement scalable, secure, and high-performance data lake architecture using services and Snowflake. • Architect and optimize object store-based data lakes using Apache Iceberg format for efficient querying, versioning, and schema evolution. • Set up and manage services including S3, Glue, Lambda, Redshift, IAM, CloudFormation, etc. • Define and enforce best practices for data modeling, ingestion, transformation, and storage. • Ensure robust data governance practices including data cataloging, lineage, access control, and compliance. • Collaborate with cross-functional teams including data engineers, analysts, and business stakeholders.