Jithendra Ch
Data Engineer • United States • j***********@gmail.com • +15******762 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Data Engineer with 4+ years of experience engineering scalable data pipelines, ETL/ELT workflows, distributed data processing, and cloud-based data solutions across banking, healthcare, and enterprise domains. Experienced in Python, SQL, PySpark, Apache Spark, Azure Databricks, Snowflake, Apache Airflow, Azure Data Factory, dbt, Delta Lake, AWS, and Microsoft Azure. Delivered reliable data platforms supporting analytics, regulatory reporting, data quality, anomaly detection, machine learning, and Generative AI initiatives.
Technical Skills
Programming Language: Python,SQL
Databases: Snowflake
Cloud Platforms: Amazon Web Services,Microsoft Azure
DevOps & Infrastructure: Azure DevOps,CI/CD
Data Engineering & Processing: PySpark,Spark SQL,Data Pipelines,Delta Lake,Apache Airflow,Azure Databricks
Data Analysis & Visualization: Power BI,Tableau
Machine Learning & AI: Anomaly Detection
Generative AI & LLMs: LangChain
Data Integration & ETL: ELT,Data Validation,Data Profiling,dbt,Azure Data Factory
Compliance & Governance: Data Quality,Data Governance
Work Experience
US Bank
United States
Data Engineer
Jan 2025 – Present
Major U.S. banking and financial-services institution where data engineering supported high-volume transactions, real-time fraud analytics, and customer insight platforms.
Tech Stack: Python, PySpark, SQL, Snowflake, Apache Airflow, AWS, ETL, Machine Learning, Data Validation, Anomaly Detection, Data Quality, Data Governance, Power BI
- Engineered scalable PySpark data pipelines to integrate high-volume banking and financial datasets, improving enterprise data availability by 40% for analytics, regulatory reporting, and fraud detection.
- Orchestrated automated Apache Airflow workflows for consumer banking and lending data, improving pipeline reliability and supporting scheduled batch processing across downstream analytics platforms.
- Optimized financial data transformations using SQL in Snowflake, reducing query execution time by 45% and accelerating reporting and analytical workloads.
- Developed governed Power BI datasets to enable stakeholders to analyze transaction activity, lending performance, fraud patterns, and portfolio metrics.
- Implemented cloud-based data processing solutions on AWS, reducing annual processing costs by $240K through workflow automation and resource optimization.
- Established automated Python data validation and anomaly-detection workflows, strengthening data quality and governance for analytics and machine learning workloads.
HCA Healthcare
United States
Data Engineer
Feb 2024 – Dec 2024
Nationwide hospital and healthcare system where data pipelines enabled analytics on electronic health records, billing, and clinical operations within compliance constraints.
Tech Stack: Azure Data Factory, Delta Lake, Apache Kafka, Spark SQL, Azure Databricks, Azure OpenAI, LangChain, dbt, Machine Learning, Data Governance, Spark Structured Streaming, ETL/ELT, Tableau, OpenAI, Generative AI
- Architected scalable Azure Data Factory pipelines with Delta Lake to integrate EHR, patient encounter, and clinical datasets, improving data accessibility by 42% for analytics and regulatory reporting.
- Streamed healthcare events through Apache Kafka and Spark Structured Streaming, enabling near-real-time clinical analytics and operational monitoring across hospital systems.
- Optimized healthcare transformation workflows using Spark SQL, improving processing performance by 38% while preparing reliable datasets for analytics and downstream machine learning initiatives.
- Developed standardized healthcare datasets and Tableau reporting models to support clinical operations, financial reporting, and executive decision-making.
- Automated scalable ELT/ ELT workflows using dbt, reducing manual processing effort by 40% and improving consistency across healthcare reporting and AI-ready datasets.
- Strengthened healthcare data governance through validation and quality controls while supporting Generative AI and LangChain initiatives using trusted clinical data.
Capgemini
India
Data Engineer
Feb 2021 – Jun 2023
Global consulting and IT services firm; built data platforms for diverse enterprise clients, integrating disparate sources to power centralized reporting and analytics.
Tech Stack: Microsoft Azure, Azure Data Factory, Azure Databricks, PySpark, SQL, Delta Lake, dbt, MLflow, Data Pipelines, Anomaly Detection, Power BI, Data Governance
- Modernized enterprise data integration using Azure Data Factory, improving processing efficiency by 40% while creating trusted datasets for centralized analytics and enterprise AI initiatives.
- Engineered scalable PySpark data pipelines to process structured and semi-structured data from multiple enterprise sources and produce standardized datasets for analytics and machine learning.
- Tuned complex transformation workflows using SQL, reducing pipeline execution time by 43% and improving SLA performance across enterprise data platforms.
- Built governed Power BI analytical datasets that enabled centralized KPI reporting and business intelligence across multiple client environments.
- Automated Azure Databricks workflows using MLflow and Delta Lake, increasing pipeline automation efficiency by 36% while supporting machine learning training and batch inference workloads.
- Automated enterprise data pipeline deployments using Azure DevOps and CI/CD pipelines, improving release reliability and reducing manual deployment effort across client data platforms.
Education
University of Cincinnati
Master of Science in Information Technology • Cincinnati, OH • Aug 2023 – Dec 2024
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