Raghavi Polishetty
Professional Summary
Senior Data Engineer with 6+ years of experience designing, building, and operating production data platforms and ETL/ELT pipelines for financial services and consulting clients. Strong in Python, PySpark, PostgreSQL, Snowflake, Databricks and Microsoft Azure with hands-on experience in Azure Data Factory and Apache Airflow orchestration. Experienced in relational data modeling, dbt-based transformations, incremental processing, source-to-target validation and reconciliation across 50+ pipelines. Delivered performance and cost optimizations including a 25% platform cost reduction and automation that eliminated 10+ hours/week of manual quality checks. Owns Git-based CI/CD with Azure DevOps, implements monitoring and runbook-driven incident response, and partners with analytics and business teams to deliver trusted dimensional datasets for reporting.
Technical Skills
Work Experience
- Designed and maintained 50+ scalable ETL/ELT pipelines using Azure Data Factory and Python to ingest REST APIs, databases and cloud files into a lakehouse.
- Built relational schemas, indexes and stored procedures in PostgreSQL to improve analytical query performance and simplify downstream access for reporting workloads.
- Developed modular PySpark components on Databricks for batch transformations and incremental processing to support frequent data refreshes and backfills.
- Orchestrated production workflows with Apache Airflow and Databricks Workflows, implementing retries, SLA checks and alerting to improve operational reliability.
- Implemented dbt transformations and Snowflake models to deliver trusted dimensional datasets consumed by analytics and BI teams.
- Optimized SQL and Spark transformation logic, contributing to a 25% reduction in platform costs by cutting inefficient compute and I/O usage.
- Automated source-to-target validation and reconciliation checks using Python, eliminating 10+ hours of manual quality work per week.
- Automated Git-based CI/CD pipelines with Azure DevOps to standardize deployments and reduce release errors while documenting runbooks for production support.
- Built and maintained ETL pipelines using Python and SSIS to ingest financial and operational data from multiple enterprise source systems.
- Backed relational data structures and complex analytical queries in PostgreSQL and SQL Server to enable extraction and reporting workloads.
- Designed dimensional models and star schemas to power Power BI dashboards and KPIs, improving access to reliable data for 50+ business users.
- Implemented automated validation and reconciliation controls to reduce data discrepancies by 15% and improve reporting reliability.
- Enhanced PySpark workloads and data partitioning strategies to reduce processing time and improve batch throughput.
- Performed root-cause analysis and executed runbook-driven production support to restore pipelines and prevent recurrence.
Education
Certifications
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