Harsha Vardhan

AI/ML • 25 years • Cincinnati, USA • UTC-05:00 • Work permits: United States • Available in 2 weeks

Azure Developer

7+ years exp

7+ years ML exp

SQL

Python

Java

Scala

Active 17 days ago

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Overview

Technical skills

Timeline

Roles

Overview

Data Engineer with 6+ years of experience designing and optimizing cloud-native data platforms and large-scale ETL/ELT pipelines across Azure, AWS, and GCP. Focused on lakehouse and streaming architectures, using PySpark, Spark, and SQL, and implementing governance and security for regulated environments. Also delivers analytics through BI tools and supports end-to-end data workflows with orchestration and infrastructure automation.

Phone

Technical skills

Languages

SQL

Python

Java

Scala

DevOps

AWS

Amazon S3

Amazon CloudWatch

AWS Lambda

Azure

Amazon Kinesis

AWS Step Functions

Terraform

CloudFormation

IAM

GCP

Databases

Google BigQuery

Amazon Redshift

Delta Lake

Databricks

PostgreSQL

Analytics

Power BI

Looker

AWS Glue

Other

Spark

Airflow

pySpark

Snowflake

Amazon SageMaker

Azure Cosmos DB

Azure SQL Database

BigQuery

Teradata

MS SQL

Apache Kafka

Hadoop

Azure DevOps

Git

Tableau

Microsoft Fabric

Azure Data Factory

CI/CD

Jenkins

Informatica

SSIS

GDPR

HIPAA

ETL/ELT

Timeline

Senior Azure Data Engineer • Senior

Google • Full-Time

Mar 2023 to Present 3 Years 7 Months In office

Built and optimized cloud-native ETL/ELT pipelines using Azure Data Factory and Microsoft Fabric, using metadata-driven approaches to improve processing efficiency. Developed lakehouse solutions on ADLS Gen2 with Delta Lake and Databricks, implementing bronze–silver–gold patterns for governance and performance. Created PySpark transformations and streaming workflows with Azure Event Hub and Fabric/Databricks for near real-time analytics, dashboards, and monitoring.

Databricks

Delta Lake

Spark

pySpark

SQL

Power BI

AWS Data Engineer • Middle

Amazon • Full-Time

Jan 2021 to Mar 2023 2 Years 2 Months In office

Designed and implemented ETL/ELT pipelines on AWS using Glue, Spark on EMR, and Step Functions to process structured and semi-structured datasets reliably. Built analytics data lakes and warehouses on S3 and Redshift, optimizing partitioning and ingestion to improve query performance and reduce costs. Implemented streaming ingestion with Kinesis plus Lambda, and automated infrastructure and security using Terraform, CloudFormation, IAM, and CloudWatch.

AWS Glue

Spark

AWS Step Functions

Amazon S3

Amazon Redshift

Amazon Kinesis

AWS Lambda

Terraform

CloudFormation

IAM

Amazon CloudWatch

Data Engineer • Middle

Oracle • Full-Time

Apr 2019 to Jan 2021 1 Year 9 Months In office

Developed ETL/ELT pipelines on Google Cloud using Dataproc/Spark and Cloud Dataflow for analytics and reporting workloads. Implemented data warehouses in BigQuery with partitioning, clustering, and materialized views to reduce execution time and storage usage. Built near real-time streaming and orchestration workflows using Pub/Sub and Cloud Composer (Airflow), and delivered analytics dashboards with Looker.

Google BigQuery

Spark

Airflow

Looker

IAM

SQL

University of Cincinnati

Master's Degree • Computer Science

Cincinnati, Ohio