Data Engineer Resume
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SUMMARY:
It is my desire to pursue a position in which I can think critically to pursue solutions while continuing to learn and develop a broader skill set.
SKILLS:
- Programming Languages:
- Python
- SQL
- PL - SQL
- Java
- PHP
- JavaScript
- C#
- ASP.NET
- ETL with SSIS
- Data Modeling
- Predictive Analytics
- Business Intelligence KPIs
- Healthcare Data knowledge
- SVN/GIT Version Control
- Amazon AWS Administration
- Regular Expressions
- Android App Development
EXPERIENCE:
Confidential
Data Engineer
Responsibilities:
- Identified and provided solutions to client inquiries by reviewing data with efficient SQL queries.
- Wrote PL/SQL scripts to perform bulk corrections to the client’s data to resolve data integrity issues.
- Reviewed, debugged, and maintained Ruby scripts used to import and process from various healthcare data sources.
- Utilized Python Data Science libraries to analyze client’s fiscal data to forecast future spending, profits, and generate reports of recommendations to decrease hospital drug expenditure.
- Knowledge of the creation of regular expressions needed to generate patterns to successfully parse data files.
- Reviewed and debugged PHP and JavaScript code used to generate reports and pull data to the client's dashboard.
- Worked major projects regarding patient data correction to resolve issues generated by inconsistent client data.
- Generated temporary data model schemas to allow for aggregation and partition logic to be applied to data subsets needed for additional analysis and data corrections.
- Developed stored procedures and functions as tools for use by fellow analysts to resolve commonly occurring issues.
- Utilized Amazon AWS to generate Ubuntu replications for use in testing of altered PHP and Javascript code blocks.
Confidential
BI ETL Developer
Responsibilities:
- Developed, maintained and optimized ETL pipelines using Microsoft SSIS for a range of clients with large and diverse datasets from various industries including healthcare, liquor distribution, and retail.
- Collaborated with fellow consultants to brainstorm and develop unique solutions for each client’s unique implementation.
- Created data models to group together fields needed to generate important reporting calculations, reduce model size, and allow for quick data refreshes.
- Developed map and reduce code to generate Python dictionaries for NoSQL data structure adherence.
- Worked in both Linux and Windows environments to implement solutions in the clients existing infrastructure.
- Utilized Confidential tools to automate script runs, file backups, and data model refreshes.
- Set up file systems, directories, and filename patterns to organize client data, models, and ad-hoc reports for ease of use.
- Maintained correspondence with clients throughout the development cycle to explain progress of the implementation.
- Developed Python scripts to parse client data and shell scripts to manage and support software execution.
- Produced detailed documentation of each implementation for the client's reference.
- Maintained Perl scripts used to configure access control per file type and dynamically configure report headers.
- Analyzed existing data trends to forecast future data progression and recommend goals.