Krithika A K

Data Engineer with 3 years of experience building scalable ETL/ELT pipelines, dimensional models, and cloud analytics solutions across enterprise and healthcare environments, leveraging SQL, Python, Spark, Azure, and GCP. Improved query performance by 81%, report accuracy by 25%, and delivered $28K+ in annual savings while supporting 1TB+ data workloads through performance optimization and data quality frameworks.

Data Engineer - Dana-Farber Cancer Institute - Boston, MA

(2025-01 - 2025-07)

• Designed a HIPAA-compliant hybrid data workflow using Informatica IICS, Python, and Bash to automate ingestion, validation, and archival of oncology disclosure files into Snowflake, replacing manual processing and reducing operational effort by 60%.
• Streamlined incident detection and recovery for 30+ daily clinical pipelines by building a three-tier orchestration framework using Tidal, Bash, and Python with REST API integrations to Datagaps and real-time Snowflake audit logging, reducing MTTR by 50% and saving $6K annually.
• Remediated Epic upgrade-related schema and metadata changes across 25+ tables by refactoring SQL transformations for 100M+ clinical records, preventing pipeline failures and preserving downstream reporting availability.
• Resolved ETL data quality issues impacting clinical reporting by debugging SQL transformation logic and performing source-to-target reconciliation across clinical datasets, improving report accuracy by 25% and reducing pipeline reruns to save $4K annually.
• Optimized Snowflake warehouse performance by tuning configurations and SQL query execution plans across large-scale workloads, reducing compute costs by 15%.

Data Engineer - Ernst & Young LLP - Bengaluru, India

(2021-03 - 2023-08)

• Partnered with cross-functional stakeholders across 20 business functions to translate business requirements into scalable cloud data solutions, defining KPIs and reporting logic across Finance, HR, Operations, Pipeline, and Production teams.
• Eliminated 15+ hours of weekly manual SAP/Excel-based reporting through analytics workflows powering 100+ Power BI dashboards, enabling daily operational insights and reducing decision time by 95%.
• Engineered ELT architecture for 1TB+ enterprise datasets from SAP ERP and PostgreSQL using Azure Synapse Pipelines, implementing CDC-based incremental refreshes and eliminating full batch loads and saving $18K annually in infrastructure costs.
• Optimized incremental data processing by developing a PySpark file detection framework in Synapse Spark, processing only newly landed files in ADLS Gen2 and reducing compute time by 85%.
• Built Synapse Dataflow transformations to cleanse and curate enterprise datasets, implementing MD5-based deduplication logic to eliminate duplicate records and improve data accuracy.
• Replaced flat table designs with a star schema in Synapse Dedicated SQL Pool, loading dimension tables with SCD Type 1 and fact tables, improving ELT performance by 30% and reducing query latency by 81%.
• Implemented SQL-based data validation, quality checks, and reconciliation routines via stored procedures across 7 pipeline stages orchestrated by ADF with Azure Monitor alerting and monitoring, achieving 98% reliability and reducing failures by 50%.
• Automated production releases through Azure DevOps CI/CD pipelines with ARM template generation, improving deployment reliability and reducing release time through infrastructure-as-code practices.
• Implemented RBAC and governance controls across analytics layers aligned with GDPR and CCPA requirements, strengthening compliant data access and reducing security incidents by 12%.

Master of Science - Data Analytics Engineering - Northeastern University (2023-09 - 2025-12) Boston, MA

Bachelor of Engineering - Computer Science - Dayananda Sagar College of Engineering (2017-08 - 2021-08) Bengaluru, India