Senior Data Engineer - Linux & Oracle Exadata Expert

Hemanth Thilak Chava — Senior Data Engineer

732-***-**** *********@*****.***

PROFESSIONAL SUMMARY:

Highly accomplished Senior Data Engineer with around 5 years of extensive experience in designing, implementing, and optimizing robust data warehousing solutions.

Expertly configure and manage Linux-based processes and infrastructure, ensuring high performance and reliability for critical data pipelines.

Proficient in developing complex Shell Scripts for automation, system administration, and enhancing various Linux- based toolsets and ETL jobs.

Deep practical experience with Oracle development, including Exadata, specializing in data load/extract processes and advanced query optimization.

Skilled in Python and Perl for data manipulation, scripting, and integrating diverse data sources within complex data ecosystems.

Adept at enhancing ETL and database load/extract processes, driving significant improvements in data flow efficiency and integrity.

Solid understanding of Unix file systems, including mount types, permissions, and standard tools, applied to intricate data management tasks.

Experienced in utilizing orchestration tools like Airflow with Python for scheduling and automating complex data warehousing workflows.

Committed to Agile methodologies and passionate about automation, continually identifying and implementing system/architecture improvements for data platforms. WORK EXPERIENCE:

Senior Data Engineer @ Tenet Healthcare Dallas, TX Sep 2024 – Present

Architected and managed Linux-based infrastructure, implementing secure processes for data warehousing solutions across a hybrid cloud environment.

Developed advanced Shell Scripts to automate critical data ingestion, transformation, and database load/extract operations, enhancing system efficiency.

Configured and optimized Oracle Exadata environments for high-performance data warehousing, ensuring robust and scalable data management.

Designed and implemented complex ETL pipelines using Python and Informatica, facilitating seamless data integration from diverse source systems.

Engineered data flow enhancements, significantly improving the performance of database load/extract processes for large-scale datasets.

Managed and optimized Unix file systems, including permissions and mount types, to support secure and efficient data storage within the data warehouse.

Orchestrated end-to-end data workflows using Airflow with Python, ensuring reliable scheduling and monitoring of all data processing jobs.

Implemented system/architecture improvements for data warehousing processes, enhancing overall platform stability and data integrity across the ecosystem.

Developed Python-based tools and scripts to automate routine administrative tasks and enhance data quality checks within the data pipelines.

Collaborated with cross-functional teams using Agile methodologies to deliver robust data solutions that meet critical business requirements.

Utilized AWS cloud services like Redshift and Glue to complement Oracle Exadata, creating a flexible and scalable data warehousing architecture.

Identified and resolved complex data performance bottlenecks within Linux and Oracle environments, ensuring optimal query execution and data availability.

Technologies Used: Linux, Shell Scripting, Oracle Exadata, Python, Informatica, Apache Airflow, AWS (Redshift, Glue, S3), SQL, Unix, Git, JIRA

Data Engineer @ Goldman Sachs New York, NY Oct 2021 – Jul 2023

Implemented and managed Linux-based data processing environments within Azure, focusing on robust infrastructure for financial data warehousing.

Developed and enhanced Shell Scripts for automated data extraction, transformation, and loading into Oracle Exadata databases, improving efficiency.

Contributed to Oracle development, optimizing database schemas and query performance to support high-volume financial analytics and reporting.

Designed and executed complex ETL processes using Informatica, integrating data from SQL Server and other sources into the data warehouse.

Leveraged Python for developing custom data processing scripts and APIs, enhancing data quality and facilitating data consumption for downstream systems.

Managed Unix file systems within the data warehousing infrastructure, applying best practices for data organization, security, and access control.

Orchestrated critical data pipelines using Airflow with Python, ensuring timely and accurate delivery of financial data for business intelligence.

Implemented system/architecture improvements for data warehousing components, focusing on scalability and reliability for regulatory reporting.

Identified and addressed performance bottlenecks in Linux environments and Oracle databases, resulting in faster data processing and query times.

Enhanced various Linux-based toolsets and processes, providing robust solutions for data governance and operational monitoring.

Collaborated extensively on data warehousing initiatives, adhering to Agile methodologies and ensuring high standards for data accuracy and security.

Developed comprehensive documentation for Linux configurations, Oracle database procedures, and ETL workflows to maintain operational excellence.

Technologies Used: Linux, Shell Scripting, Oracle Exadata, Python, Informatica, Apache Airflow, Azure (ADF, ADLS, Synapse), SQL, Unix, Git, JIRA

Data Engineer @ Big Lots Columbus, OH Dec 2019 – Sep 2021

Implemented and managed Linux-based environments on GCP, supporting data ingestion and processing for retail data warehousing initiatives.

Developed robust Shell Scripts for automating file transfers, data validation, and scheduling routine operational tasks within the data platform.

Designed and implemented scalable ETL pipelines using Python and PySpark on GCP Dataflow, processing large volumes of retail transaction data.

Ingested diverse datasets from MongoDB and flat files into GCP Cloud Storage, preparing data for analysis and reporting within the data warehouse.

Transformed raw data into structured formats using PySpark and SQL, ensuring data quality and consistency for downstream applications.

Loaded processed data efficiently into Google BigQuery, optimizing queries and schemas for improved analytical performance and business insights.

Orchestrated complex data workflows using Cloud Composer (Airflow) with Python, ensuring timely updates for inventory and sales reporting.

Managed Unix file systems on cloud storage, implementing best practices for data organization, security, and version control for large datasets.

Contributed to the data warehousing architecture, identifying opportunities for system improvements and automation to enhance data delivery.

Implemented data validation and cleansing routines, ensuring high data integrity and reliability across all data pipelines.

Collaborated effectively with business intelligence teams to deliver insightful dashboards and reports using the integrated data warehouse.

Documented data models, ETL processes, and Linux configurations, ensuring comprehensive knowledge transfer and system maintainability.

Technologies Used: Linux, Shell Scripting, Python, Apache Airflow (Cloud Composer), GCP (BigQuery, Dataflow, Cloud Storage), PySpark, SQL, Unix, Git, JIRA

TECHNICAL SKILLS:

Operating Systems: Linux, Unix

Programming Languages: Python, Shell Scripting, SQL, Perl, Scala

Databases: Oracle Exadata, PostgreSQL, MySQL, Hive, Amazon Redshift, Azure Synapse, Google BigQuery, DynamoDB, MongoDB

Data Warehousing & ETL: Data Warehousing, ETL Development, Informatica, AWS Glue, Azure Data Factory, GCP Dataflow, Spark, Hadoop, Databricks, Snowflake

Orchestration & Workflow: Apache Airflow, AWS Step Functions, GCP Cloud Composer

Cloud Platforms: AWS, Azure, Google Cloud Platform (GCP)

Version Control & CI/CD: Git, GitHub, Jenkins, Docker

Methodologies & Tools: Agile, JIRA, Confluence, Tableau, Lucidchart EDUCATION:

Master of Science in Computer Science @ University of New Haven

Contact this candidate