Business Intelligence Developer Resume

SUMMARY

Goal - driven, methodical, and data-solutions-oriented professional with four years hands-on experience in data analytics and business intelligence. Certified by Amazon Web Services (AWS) and Tableau. Concept-to-execution solutions by creating high-quality, under-budget, and timely completion of projects. Expert at overseeing data strategies, developing business intelligence solutions, and conceptualizing plans toward data driven insights and attainment of organizational objectives.

TECHNICAL SKILLS

SQL | Python | Pandas | NumPy | SciPy | Matplotlib | Seaborn | Kepler | SciKit-Learn | Jupyter Notebooks | Opencv | XGBoost
Tableau | Power BI | AWS Aurora | AWS RedShift | AWS Athena | AWS Glue | AWS Lambda | AWS SageMaker | Microsoft Excel
SQL Server | SQL Server Integration Services (SSIS) | Microsoft Azure | Amazon Web Services (AWS) | API

PROFESSIONAL EXPERIENCE

Confidential

Business Intelligence Developer

Responsibilities:

Reduced communication errors by partnering with finance and business development teams to create intuitive and interactive sales pipeline dashboards in Tableau;
Executed the creation of an enterprise asset utilization dashboard for operations management;
Improved data analytics adoption among employees by embedding Tableau dashboards and data visualizations into internal portals;
Utilized web data connectors to gain timely insights by extracting data from public sources for use in centralized Tableau dashboards
Created data visualizations to evaluate customer behavior, demographics, and discover growth opportunities using Python packages such as Matplotlib, Seaborn, and Kepler
Extracted data from various web sources via web scraping and API using Python
Data mining and modeling:
Uncovered untapped marketing opportunities for independent contractors in the energy sector using Python
Analyzed customer and product information in a cloud-based SQL Server
Utilized statistical methods and machine learning libraries to develop predictive models for consumer behavior
Cleaned and transformed data at scale by using Python packages NumPy, Pandas, and Sklearn
Created Key Performance Indicators (KPIs) to evaluate data quality as part of a long-term initiative for improving data analytics operations
Improved the quality of data and analytics by performing cleansing, de-duplication, and harmonization of data across various systems, both on-premise and cloud-based in Amazon Web Services

Confidential

Senior Data Analyst / Senior Business intelligence Analyst

Responsibilities:

Strategically partnered with cross-functional teams to structure problems, identify appropriate data sources, extract data, and develop integrated information delivery solutions
Efficiently developed the following:
System integration models, specifications, data flow diagrams, and charts to guide analysts and developers;
Key performance indicators (KPIs) to increase employee utilization and minimize overtime hours; and
An enterprise data architecture roadmap to improve the effectiveness of data management and analytics
Created custom reports and data visualizations to support operations teams in multiple time zones and regions
Created predictive models used for pre-project profitability assessment to standardize project bid analysis
Executed SQL scripts to gather, clean, and manipulate data in both on-premise and cloud-based SQL Servers
Performed multiple initiatives using Microsoft SQL Server, Azure, Tableau, and Microsoft Visio
Supported the establishment of data strategy that provided new insights for the organization by adopting new data structures, hierarchies, and data governance principles; implementing new systems and ensuring process controls

Confidential

Data Analyst / Business Intelligence Analyst

Responsibilities:

Collaborated with interdisciplinary teams and stakeholders to build effective data gathering processes and assess requirements for data solutions
Created the following reports and models:
Board of manager’s presentation materials and reports on the status of process and data systems implementation throughout the region and company;
Process and software return on investment (ROI) reports for executive-level managers; and
Developed, implemented, and evaluated predictive models to determine asset utilization, as well as forecast project profitability
Assessed customer relationship management (CRM) solutions via requirements and current and future state analysis
Business Intelligence (BI):
Steered efforts in delivering business intelligence software for data manipulation, financial reporting visualization, and hidden cost drivers tracking
Evaluated business intelligence platforms - Tableau, Domo, Power Bi, and Looker
Created dashboards and data visualizations to aid regional managers in operational oversight.
Played an integral role in implementing new reporting processes and control measures which increased equipment use and reduced equipment rental and materials expenditures