Senior Data Engineer - Oracle & ETL Expert

Teja M — Senior Data Engineer

972-***-**** ***************@*****.***

PROFESSIONAL SUMMARY:

Highly experienced Data Warehouse Engineer with around 5 years of proven expertise in designing and optimizing robust Linux-based data warehousing infrastructure and processes.

Adept at implementing system and architecture improvements, enhancing Linux-based toolsets, and developing powerful shell scripts for automation.

Specialized in enhancing ETL and database load/extract processes, ensuring high performance and data integrity across complex data ecosystems.

Proficient in Oracle development and administration, with practical working experience in relational databases and advanced SQL for data manipulation.

Strong command of Python and Shell Scripting for large-scale data processing, automation, and orchestration within Linux environments.

Expert in developing and managing sophisticated ETL solutions, with practical experience utilizing tools such as Informatica and modern cloud-native services.

Experienced in setting up and managing Linux environments, possessing practical knowledge of Unix file systems, permissions, and standard command-line tools.

Passionate about driving automation and continual process improvement, consistently delivering efficient and scalable data solutions adhering to Agile methodologies.

Skilled in orchestration tools like Apache Airflow with Python, streamlining complex data workflows and ensuring reliable data delivery for business intelligence. EDUCATION:

Master of Science in Computer Science @ Texas Tech University TECHNICAL SKILLS:

Programming Languages: Python, SQL, Shell Scripting, Perl

Data Warehousing: Oracle Exadata, Snowflake, Redshift, Azure Synapse Analytics, Hive

ETL Tools: Informatica, Azure Data Factory, AWS Glue, Apache Spark (PySpark)

Orchestration & Automation: Apache Airflow, Jenkins, Docker

Cloud Platforms: AWS (S3, EMR, Glue, Lambda), Azure (ADLS Gen2, Data Lake Analytics), Linux Environment Setup

Database Management: Oracle, PostgreSQL, Azure SQL Database, MySQL

Version Control & Collaboration: Git, JIRA, Confluence

Big Data Technologies: Hadoop, Databricks

Operating Systems: Linux, Unix File Systems

WORK EXPERIENCE:

Senior Data Engineer @ Elevance Health Indianapolis, IN Sep 2024 – Present

Designed and implemented scalable Linux-based data warehousing infrastructure, optimizing processes for healthcare claims and membership data on AWS.

Developed robust shell scripts and Python utilities to automate data ingestion, transformation, and load processes across diverse data sources.

Enhanced ETL and database load/extract processes using Informatica, ensuring efficient data flow into analytical data marts and operational data stores.

Configured and managed Linux environments for data processing, applying practical knowledge of Unix file systems and permissions for secure operations.

Implemented system and architecture improvements, designing highly available data pipelines leveraging Apache Airflow with Python for complex workflow orchestration.

Leveraged Oracle development skills to integrate high-volume transactional data, performing advanced SQL tuning for optimal query performance and data integrity.

Developed PySpark-based ETL pipelines on AWS EMR, processing large datasets and integrating with Oracle Exadata for high-performance analytical queries.

Automated CI/CD pipelines using Jenkins and Docker for containerized ETL applications, ensuring rapid and reliable deployment of data solutions.

Implemented comprehensive data quality checks and validation frameworks, upholding data accuracy for critical healthcare reporting and compliance.

Enabled robust data consumption through Athena queries and Power BI dashboards, providing actionable insights to business stakeholders.

Participated actively in Agile ceremonies, using JIRA for sprint planning and documenting complex data architectures with Confluence and Lucidchart.

Drove continual process improvement initiatives, identifying bottlenecks and implementing automation solutions to enhance data pipeline efficiency and reliability. Technologies Used: Linux, Shell Scripting, Oracle Exadata, Informatica, Python, Apache Airflow, AWS (S3, EMR, Glue, Redshift), PySpark, Docker, Jenkins, Power BI, Git, JIRA Data Engineer @ U.S. Bank Minneapolis, MN Apr 2022 – Jul 2023

Designed and implemented Azure-based data warehousing solutions, leveraging Linux environments for managing core data processes and infrastructure.

Developed complex shell scripts to automate data movement and operational tasks within Azure Data Lake Storage Gen2, optimizing file system management.

Enhanced ETL processes using Azure Data Factory and Informatica, migrating on-prem Oracle financial data warehouse components to the Azure cloud.

Performed extensive Oracle development, including advanced SQL queries and stored procedures, to manage critical financial transaction data efficiently.

Built robust PySpark transformations within Azure Databricks, processing large financial datasets and integrating with Oracle Exadata for specialized analytics.

Implemented incremental data loading strategies and performance tuning techniques across Azure SQL Database and Synapse Analytics to ensure scalability.

Applied practical knowledge of Unix file systems for secure data handling and processing within the Azure Linux virtual machines and services.

Automated scheduling and monitoring using ADF triggers and pipelines, integrating with Python scripts for enhanced orchestration capabilities.

Created dimensional data models for regulatory and risk reporting, ensuring data integrity and compliance for sensitive financial information.

Integrated Tableau dashboards with Azure Synapse Analytics for executive reporting, providing clear visualizations of key financial metrics.

Applied rigorous data validation and reconciliation processes to ensure the accuracy and completeness of financial datasets for regulatory compliance.

Utilized Git for source code management and actively participated in Agile Scrum methodology, delivering high-quality data engineering solutions.

Technologies Used: Linux, Shell Scripting, Oracle Exadata, Informatica, Python, Azure (ADLS Gen2, Data Factory, Synapse Analytics, Azure SQL Database), Databricks (PySpark), Tableau, Git, JIRA Junior Data Engineer @ Wayfair Boston,MA Nov 2019 – Mar 2022

Designed and developed efficient ETL workflows for retail sales and inventory datasets, leveraging Informatica PowerCenter for robust data integration.

Developed comprehensive shell scripts to automate batch data ingestion from various source systems into PostgreSQL, ensuring timely data availability.

Implemented data cleansing and transformation logic using Python scripts, enhancing data quality for critical business intelligence reporting.

Created star schema data models to support detailed sales trend and customer behavior analysis, optimizing data structures for reporting tools.

Developed advanced SQL queries and stored procedures in PostgreSQL, significantly improving the performance of data aggregation processes.

Performed extensive data validation and reconciliation between source and target systems, ensuring data accuracy and consistency across the data warehouse.

Implemented indexing and query optimization techniques within PostgreSQL, leading to a 25% improvement in reporting query execution times.

Integrated Tableau dashboards for sales and operational reporting, providing intuitive visualizations for key business performance indicators.

Maintained strict version control using Git for all data engineering artifacts and coordinated releases via Jenkins, ensuring controlled deployments.

Provided comprehensive production support, swiftly addressing data issues and implementing defect fixes in close coordination with QA teams.

Configured Linux environments for ETL job execution, ensuring optimal resource utilization and system stability for critical data processing tasks.

Contributed to Agile development processes, working collaboratively to gather requirements, design solutions, and deliver high-impact data initiatives.

Technologies Used: Linux, Shell Scripting, Informatica PowerCenter, Python, SQL, PostgreSQL, Tableau, Jenkins, Git

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