Snowflake Data Engineer - R01568220

As a Data Engineer at Brillio, you will be at the forefront of building robust, scalable data architectures that power complex business intelligence and AI initiatives. You will work closely with cross-functional teams, including AI/ML engineers and product stakeholders, to transform raw data into reliable, high-quality assets. Your day-to-day will involve architecting sophisticated data pipelines, implementing advanced modeling techniques, and ensuring the seamless flow of information across the enterprise, directly influencing the data-driven decision-making capabilities of our clients.

Snowflake, Airflow, DBT

SQL

Python/PySpark

ETL

CDC

Snowflake 

SnowPro

→Design and develop scalable data pipelines and modern data architectures using Snowflake.

→Lead migration initiatives from legacy databases to Snowflake.

→Build and optimize ELT frameworks using DBT.

→Develop and orchestrate workflows using Apache Airflow.

→Design and implement data models, including Star Schema and Snowflake Schema.

→Develop ingestion pipelines for structured and semi-structured data.

→Create reusable frameworks for data validation, reconciliation, auditing, and monitoring.

→Optimize Snowflake objects, including:

→Virtual Warehouses

→Clustering Keys

→Materialized Views

→Streams & Tasks

→Dynamic Tables

→Develop technical design documents and migration runbooks.

→Perform code reviews and enforce engineering best practices.

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→Assess and migrate data from legacy platforms, including:

→Oracle

→SQL Server

→Teradata

→Netezza

→DB2

→SAP HANA

→On-Prem Data Warehouses

→Perform source system analysis and database rationalization.

→Design migration strategies, including:

→Full Data Loads

→Incremental Loads

→CDC (Change Data Capture)

→Historical Data Migration

→Conduct data reconciliation and validation during migration.

→Optimize transformed workloads for Snowflake architecture.

→Execute cutover planning and post-migration stabilization.

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→Provide L3 support for Snowflake data platforms and pipelines.

→Monitor and manage production data workloads.

→Troubleshoot data failures, performance issues, and pipeline disruptions.

→Perform root cause analysis (RCA) and implement permanent fixes.

→Support incident, problem, change, and release management activities.

→Work closely with business users and stakeholders to resolve production issues.

→Ensure adherence to SLA and operational support commitments.

→Participate in production releases and deployment activities.