G Yashwanth
Professional Summary
Data Engineer with 4+ years of experience building and maintaining scalable, automated ETL/ELT pipelines across Azure and AWS cloud data platforms. Hands-on experience with Apache Spark, PySpark, Spark SQL and Databricks for large-scale data processing, plus SQL-based dimensional data modeling (star schema) to support reporting and analytics. Skilled in data quality, metadata and lineage practices, serverless ingestion patterns, and partnering with BI teams to deliver analytics-ready datasets.
Technical Skills
Work Experience
- Engineered and maintained automated ETL pipelines using Azure Data Factory and Databricks to process multi-terabyte finance and operations datasets while meeting defined SLA requirements.
- Built PySpark and Spark SQL transformations with structured error handling and reconciliation checks to improve dataset reliability for downstream analytics consumers.
- Implemented metadata, lineage and reference-data validation processes to detect schema drift and data anomalies before datasets reached reporting layers.
- Deployed and tuned production Spark and Synapse workloads to improve query response times and ensure availability for leadership Power BI reports.
- Collaborated with BI and business stakeholders to design dimensional (star-schema) datasets and data marts that enabled self-service reporting and reduced ad-hoc SQL requests.
- Integrated Azure ML-based anomaly checks into ETL flows and automated alerting to reduce time-to-detect data quality issues.
- Built AWS Glue ETL pipelines to ingest structured and semi-structured campaign and operational data from S3 and APIs into Redshift, enabling analytics for marketing and ops teams.
- Developed serverless AWS Lambda functions for API ingestion with automated retries and centralized logging to improve pipeline resilience and reduce manual intervention.
- Applied Python-based anomaly detection and automated data-quality checks to surface unexpected metric spikes and breaks prior to BI consumption.
- Designed dimensional Redshift schemas with optimized distribution and sort keys to improve query performance for Power BI and Tableau dashboards.
- Integrated Snowflake as an analytical layer for selected datasets and orchestrated data movement between Snowflake and Redshift for downstream reporting.
- Supported analysts with regular and ad-hoc SQL analysis to validate datasets and align warehouse outputs with forecasting and reporting requirements.
- Designed and maintained dimensional data models and analytical schemas using Azure SQL Database and Azure Synapse to support operational and management reporting across business functions.
- Developed Python-based statistical and predictive models and integrated outputs into Power BI dashboards for trend analysis and forecasting.
- Built optimized Power BI reports with advanced DAX calculations and data model tuning to enable self-service KPI exploration and reduce report load times.
- Implemented ETL workflows with Azure Data Factory to consolidate data from multiple RDBMS sources into centralized, analytics-ready datasets with quality checks.
- Validated data accuracy and model outputs through Jupyter-based analysis and collaborated with stakeholders to translate insights into operational action.
- Supported Hadoop-based batch processing where required and maintained ad-hoc SQL query support for business and forecasting teams.
Education
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