Rakesh P

• Architected Medallion Architecture (bronze/silver/gold) on Azure and Snowflake, processing 1M+ healthcare records daily for clinical analytics using Delta Lake and Apache Iceberg. • Designed and maintained scalable ETL/ELT pipelines using Snowflake, dbt Cloud, and Azure Data Factory, enforcing Data Contracts and schema evolution policies to prevent breaking changes across downstream dependencies. • Implemented Infrastructure as Code (IaC) using Terraform to automate provisioning of Snowflake objects, cloud resources, and RBAC roles reducing provisioning time by 60%. • Optimized Snowflake performance through advanced clustering keys, Z-ordering and automatic clustering, achieving 45% reduction in query latency for complex analytical queries. • Refactored inefficient SQL queries and redesigned data models, achieving 35% reduction in overall pipeline runtime and improved SLAs for downstream consumers. • Established comprehensive data quality framework using dbt and Great Expectations with automated validation, anomaly detection, and data profiling reducing critical data discrepancies by 40%. • Implemented HIPAA-compliant security controls including Row-Level Security (RLS), dynamic data masking, ColumnLevel Security, and encryption at rest and in transit for 2M+ sensitive patient records. • Built real-time data observability dashboards tracking pipeline health, data freshness, volume trends and quality metrics for proactive incident response using Power BI. • Integrated LLM-based pipelines for automated extraction and summarization of unstructured clinical notes, enhancing data availability for advanced analytics and research. Environment: Azure, Snowflake, dbt Cloud, Azure Data Factory, Terraform, Python, PySpark, Apache Airflow, Great Expectations, HIPAA, Delta Lake, Apache Iceberg, Parquet, Git, GitHub Actions, Power BI

• Designed and delivered scalable data pipelines processing 5TB+ daily across AWS and GCP, supporting critical analytics and operational reporting for 4 engineering teams across multi-cloud environments. • Built distributed data transformations using Python, PySpark, and SQL on Snowflake and BigQuery, handling complexjoins, schema evolution and large-scale aggregations for business intelligence. • Automated cloud infrastructure provisioning using Terraform, managing S3 buckets, IAM roles, Glue jobs, VPC configurations, and secrets management reducing manual deployment effort by 50%. • Orchestrated batch, micro-batch, and Spark Streaming pipelines using Apache Airflow and Apache Kafka with EventDriven Architecture, designing DAGs with Change Data Capture for late-arriving data handling. • Optimized SQL query performance through refactoring, join restructuring, and workload tuning achieving 35% reduction in pipeline runtime and measurable cloud cost savings. • Established Data Lineage tracking and governance standards across multi-cloud pipelines using Unity Catalog, improving end-to-end traceability from source ingestion to final reporting. • Implemented data quality validation frameworks using Great Expectations to detect anomalies, duplicates, and inconsistencies in high-volume Spark Streaming and batch data. • Collaborated with analytics teams, data scientists and business stakeholders to understand requirements and deliver reliable, production-scale data systems. • Configured Snowflake and BigQuery warehouses for cost-performance balance, implementing auto-scaling, clustering, partitioning, and Spark Streaming optimizations. Environment: AWS (S3, Glue, Kinesis, IAM, VPC), GCP (BigQuery, Cloud Storage), Snowflake, Unity Catalog, Terraform, Apache Airflow, Python, PySpark, SQL, Apache Kafka, Spark Streaming, Git, GitHub Actions, Tableau

• Designed and delivered enterprise-scale financial data pipelines and data warehouses using Snowflake, dbt, and AWS for a global investment banking client, supporting risk analytics, regulatory reporting (Basel III, CCAR), and trade reconciliation across equities and fixed income portfolios. • Built and maintained ETL/ELT pipelines ingesting 5TB+ of daily trade, position, and market data from upstream systems (Bloomberg, Reuters, internal OMS) into a centralized Snowflake data warehouse for P&L and risk attribution reporting. • Applied Change Data Capture patterns using Databricks and EMR to enable real-time ingestion of transaction ledger updates, reducing end-of-day reconciliation latency by 35%. • Implemented Infrastructure as Code (IaC) with Terraform across development, staging, and production environments, standardizing cloud resource provisioning and reducing deployment failures by 45%. • Refactored complex legacy SQL queries and stored procedures for financial reporting models optimizing join logic, eliminating redundant calculations, and achieving 40% reduction in report generation time for daily risk dashboards. • Enforced data governance controls including Column-Level Security for PII and sensitive financial data, Data Lineage documentation from source trades to aggregated risk positions, and RBAC-based access policies aligned with SOX compliance requirements. • Designed Star Schema and Snowflake Schema dimensional models for financial reporting covering trade lifecycle, counterparty exposure, and portfolio performance improving query performance by 25%. Environment: AWS (S3, Redshift, Glue, IAM), Snowflake, dbt, Terraform, Python, SQL, Apache Airflow, Databricks, Great Expectations, Git, Agile, Jira, Confluence, Tableau

• Developed and maintained data integration solutions for a large financial services client, automating ingestion of trade confirmations, account statements and transaction records from multiple upstream banking systems into a centralized SQL data warehouse. • Built reusable Python modules and REST API integrations to extract structured and semi-structured financial data (CSV, JSON, XML) from third-party vendors and core banking platforms, implementing error handling, pagination and rate limiting for reliable daily loads. • Wrote and optimized complex SQL queries, stored procedures, indexes and views to support financial reporting and reconciliation workflows achieving 25% improvement in query execution times and reducing report generation lag. • Developed data validation and cleansing routines to enforce business rules on incoming financial records flagging duplicate transactions, null account identifiers and type mismatches before loading into downstream reporting tables. • Automated ETL/ELT job scheduling and monitoring using Bash scripting and Cron Jobs on Linux servers, implementing structured logging and alerting to enable proactive failure detection and reduce manual intervention. • Supported software development lifecycle (SDLC) activities including unit testing, code reviews, and deployment scripting, contributing to on-time delivery of 3 data integration modules in an Agile team environment. • Documented data flow diagrams, transformation logic and API integration specifications, building foundational data engineering knowledge that enabled a transition into full-scale financial data pipeline. Environment: Python, Pandas, SQL, MySQL, PostgreSQL, REST APIs, CSV, JSON, XML, Git, Jira, Agile, Linux, Bash, Cron Jobs