RIYAZ Naikodi

Azure Snowflake Data Engineer @ Citi

About

I’m a Senior Data Engineer with 11+ years of experience designing scalable, cloud-native data platforms across finance, healthcare, and retail domains. I specialize in building high-performance data pipelines and real-time analytics solutions using Azure, Snowflake, and Databricks. My expertise includes developing modern ETL/ELT frameworks, optimizing large-scale data processing, and implementing robust data governance and security practices. Over the years, I’ve successfully: Built scalable data lake and warehouse architectures on Azure and Snowflake Designed real-time streaming pipelines processing millions of events Improved pipeline performance and reduced costs through optimization strategies Led migrations from legacy systems to modern cloud platforms Delivered impactful dashboards and insights using Power BI I’m highly skilled in technologies like Azure Data Factory, Databricks, Snowflake, PySpark, DBT, and Delta Lake, with a strong focus on data quality, performance, and reliability. I enjoy solving complex data challenges, enabling data-driven decision-making, and continuously exploring new innovations in cloud and big data technologies.

Country

United States

City

New Jersey

Industry

Information Technology & Services

Skill

Data Integration, Continuous Integration and Continuous Delivery (CI/CD), Databases, Apache Spark Streaming, Microsoft Power BI, Data Modeling, PySpark, Snowflake, Azure Data Factory, Azure Databricks, Docker Products, Big Data, Amazon Web Services (AWS), Azure Data Lake, Jenkins, Data Structures, Terraform, MapReduce, Extract, Transform, Load (ETL), Microsoft Azure

Experience

Azure Snowflake Data Engineer

Citi

2023-9 - Present · 3 yrs 1 mo

Tampa, Florida, United States

Designed and optimized scalable ETL/ELT pipelines using Azure Data Factory, Databricks, and Matillion, improving data throughput and reducing latency Built real-time data streaming solutions using Azure Event Hub and Spark Streaming for low-latency analytics Engineered data lake architecture using Azure Data Lake Gen2 and Delta Lake with Medallion Architecture Developed modular data transformations using DBT, PySpark, and SQL ensuring data quality and lineage Optimized Snowflake performance using clustering, partitioning, and warehouse tuning Implemented Delta Live Tables (DLT) for automated data validation, schema evolution, and error handling Integrated multi-source data (CRM, ERP, web analytics) into a unified Customer Data Platform (CDP) Built interactive Power BI dashboards with real-time insights using DAX and DirectQuery Automated CI/CD pipelines and infrastructure provisioning using Terraform and Azure DevOps Led migration of legacy systems to Azure, Databricks, and Snowflake platforms Strengthened data security using Azure Key Vault, Unity Catalog, and RBAC policies

Azure Data Engineer

Optum

2021-6 - 2023-9 · 2 yrs 4 mos

New York City Metropolitan Area

Developed scalable data pipelines using Azure Data Factory and Databricks with Delta Lake architecture Built real-time streaming pipelines using Azure Event Hubs and Databricks Structured Streaming Designed and implemented data governance frameworks using Microsoft Purview and Unity Catalog Optimized ETL workflows across Azure Synapse and Snowflake, improving processing efficiency Automated data ingestion using ADF and Databricks Auto Loader for large-scale datasets Created advanced data models and transformations using DBT and Snowflake Implemented Snowpipe for continuous real-time data ingestion Built Power BI dashboards with real-time analytics and advanced DAX calculations Tuned Spark jobs and SQL queries, improving performance and reducing execution time Automated CI/CD pipelines using Azure DevOps and Git for seamless deployments Ensured data security using Azure Key Vault and enterprise-grade encryption

Data Engineer

Johnson & Johnson

2019-12 - 2021-6 · 1 yr 7 mos

San Francisco, California, United States

Designed healthcare data pipelines using Databricks, PySpark, and SQL for large-scale data processing Built data models using Star and Snowflake schemas for healthcare analytics Ensured compliance with HIPAA, HL7, and FHIR standards for secure data handling Developed real-time streaming pipelines using Kafka and Spark Streaming Automated ETL workflows using Apache Airflow and Python Migrated legacy data from Oracle and SQL Server to Hadoop and Databricks platforms Optimized data processing performance using Spark and SQL tuning techniques Built Power BI dashboards for healthcare reporting and analytics Implemented data quality checks and validation processes for accurate reporting