Data Engineering and analytics analyst with 3.5+ years of experience building scalable ETL/ELT pipelines. Experience in SQL Python , Snowflake, PySpark, and advanced performance tuning. Proven track record of processing…
Data Engineering and analytics analyst with 3.5+ years of experience building scalable ETL/ELT pipelines. Experience in SQL Python , Snowflake, PySpark, and advanced performance tuning. Proven track record of processing and analyzing terabytes of data on GCP and Azure ecosystems. Adept at designing governance frameworks and delivering executive-level BI solutions using Looker and Power BI.
Architected a serverless ETL pipeline using Cloud Composer (Airflow) and Dataflow to migrate terabytes of legacy logs into a BigQuery warehouse, ensuring unified schema standardization.
Designed a BigQuery data model with date-partitioning and clustering strategies, reducing query slot consumption by 40% and optimizing SQL performance for global dashboards.
Implemented granular Access Control Lists (ACLs) and Row-Level Security to separate raw ingestion layers from curated reporting views, ensuring PII compliance across engineering teams.
Deployed a custom alerting framework using Airflow callbacks to reduce Mean Time to Resolution (MTTR) for pipeline incidents and automated validation scripts to quarantine corrupt files.
ETL Pipeline Architecture: Architected a serverless ETL pipeline using Cloud Composer (Airflow) and Dataflow to migrate terabytes of legacy logs into a BigQuery warehouse, ensuring unified schema standardization., SQL & Performance Tuning: Designed a BigQuery data model with date-partitioning and clustering strategies, reducing query slot consumption by 40% and optimizing SQL performance for global dashboards., Data Governance & Security: Implemented granular Access Control Lists (ACLs) and Row-Level Security to separate raw ingestion layers from curated reporting views, ensuring PII compliance across engineering teams., Operational Reliability: Deployed a custom alerting framework using Airflow callbacks to reduce Mean Time to Resolution (MTTR) for pipeline incidents and automated validation scripts to quarantine corrupt files.
Developed reusable data transformation scripts in Databricks (using PySpark notebooks) to streamline data validation and ingestion of large-scale datasets, establishing a robust foundation for downstream analytics.
Improved data pipeline performance by 20–30% through advanced SQL techniques including sharding, query hints, hash joins, and table functions.
Automated key business dashboards in Looker Studio, reducing manual reporting time by 5+ hours per month and providing real-time visibility into marketing KPIs.
Collaborated with cross-functional teams to identify data bottlenecks, resulting in a streamlined ingestion process that supported rapid scaling of marketing data feeds.
Databricks Engineering: Developed reusable data transformation scripts in Databricks (using PySpark notebooks) to streamline data validation and ingestion of large-scale datasets, establishing a robust foundation for downstream analytics., Advanced SQL Optimization: Improved data pipeline performance by 20–30% through advanced SQL techniques including sharding, query hints, hash joins, and table functions., Automated Reporting: Automated key business dashboards in Looker Studio, reducing manual reporting time by 5+ hours per month and providing real-time visibility into marketing KPIs., Process Improvement: Collaborated with cross-functional teams to identify data bottlenecks, resulting in a streamlined ingestion process that supported rapid scaling of marketing data feeds.
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