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Hi, I’m Pranathi Pashikanti, a data engineer with 3+ years of experience building scalable batch ETL pipelines using PySpark, SQL, AWS Glue, and Airflow to transform large structured and semi-structured datasets into analytics-ready assets for reporting, compliance, and BI. I specialize in analytics data models in Snowflake and Redshift, ensure data quality with validation frameworks like dbt, and optimize Spark performance. I enjoy collaborating with analysts and stakeholders and applying CI/CD, Git, and cloud-native tools to deliver trusted, production-grade data solutions.…Hi, I’m Pranathi Pashikanti, a data engineer with 3+ years of experience building scalable batch ETL pipelines using PySpark, SQL, AWS Glue, and Airflow to transform large structured and semi-structured datasets into analytics-ready assets for reporting, compliance, and BI. I specialize in analytics data models in Snowflake and Redshift, ensure data quality with validation frameworks like dbt, and optimize Spark performance. I enjoy collaborating with analysts and stakeholders and applying CI/CD, Git, and cloud-native tools to deliver trusted, production-grade data solutions.

Pranathi Pashikanti

Full Stack Developer, Web Developer, Programmer, +2

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Hi, I’m Pranathi Pashikanti, a data engineer with 3+ years of experience building scalable batch ETL pipelines using PySpark, SQL, AWS Glue, and Airflow to transform large structured and semi-structured datasets into analytics-ready assets for reporting, compliance, and BI. I specialize in analytics data models in Snowflake and Redshift, ensure data quality with validation frameworks like dbt, and optimize Spark performance. I enjoy collaborating with analysts and stakeholders and applying CI/CD, Git, and cloud-native tools to deliver trusted, production-grade data solutions.…Hi, I’m Pranathi Pashikanti, a data engineer with 3+ years of experience building scalable batch ETL pipelines using PySpark, SQL, AWS Glue, and Airflow to transform large structured and semi-structured datasets into analytics-ready assets for reporting, compliance, and BI. I specialize in analytics data models in Snowflake and Redshift, ensure data quality with validation frameworks like dbt, and optimize Spark performance. I enjoy collaborating with analysts and stakeholders and applying CI/CD, Git, and cloud-native tools to deliver trusted, production-grade data solutions.

Available to hire

Hi, I’m Pranathi Pashikanti, a data engineer with 3+ years of experience building scalable batch ETL pipelines using PySpark, SQL, AWS Glue, and Airflow to transform large structured and semi-structured datasets into analytics-ready assets for reporting, compliance, and BI.

I specialize in analytics data models in Snowflake and Redshift, ensure data quality with validation frameworks like dbt, and optimize Spark performance. I enjoy collaborating with analysts and stakeholders and applying CI/CD, Git, and cloud-native tools to deliver trusted, production-grade data solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

Data Engineer at Morgan Stanley

June 1, 2024 - Present

Designed and developed scalable batch ETL pipelines using PySpark, AWS Glue, and SQL to process structured financial datasets for analytics, compliance, and reporting. Built and maintained Airflow DAGs to orchestrate ingestion and transformation with dependencies, retries, and SLA monitoring. Implemented data validation and reconciliation checks using SQL and dbt tests to ensure data accuracy. Modeled analytics-ready datasets in Snowflake and Redshift, supporting dashboards and ad-hoc analysis. Integrated data from S3, relational databases, and external APIs; transformed raw inputs into curated datasets. Optimized pipelines with Databricks Lakehouse, Delta Lake, and Spark SQL across ADLS Gen2, Azure Blob, Snowflake, and relational sources. Automated deployment with Git and CI/CD. Monitored pipelines with CloudWatch; collaborated with analysts to define data requirements. Authored documentation for pipelines and runbooks.

Data Engineer at DXC Technology

July 1, 2021 - January 1, 2023

Designed, developed, and supported cloud-based data processing applications to ingest, transform, and load large-scale datasets for analytics and downstream systems. Implemented backend data services using Python, PySpark, and SQL. Built automated ingestion workflows using Azure Data Factory and AWS Glue. Applied ontology-driven thinking to align data models with business entities and metrics. Developed robust error handling, logging, and retry mechanisms to improve stability. Enforced data governance using Unity Catalog, enabling RBAC and enterprise policies. Deployed and managed Databricks jobs and configurations with GitLab CI/CD across dev/test/prod. Collaborated with cross-functional teams to integrate backend systems with BI tools and ensure data accuracy.