I have over 13 years of experience as a Data Engineer, Software Engineer, and Analyst focused on big data applications. I apply my data, software, and database skills to produce in-demand data products. I use my experience in data quality and integration to create data pipelines, services, and connectors used by data scientists and data analysts. I am a problem solver. Unknown or unique problems don't scare me. I have self-starter and self-learning skills that distinguish me from others. I’m a curious person who enjoys the investigative aspect of examining problems and evolving solutions to find just the right fit. I am adaptable and quickly learn processes, practices, and technologies. I have just completed a master's degree in software engineering specializing in Cloud computing and Big Data. I selected these disciplines to formulize and strengthen my knowledge and skills as both a data engineer and a software engineer. Highlights of my accomplishments include:• Capturing and manipulating huge and complex volumes of data for use by investment managers to measure a company's performance and understand how similar businesses compare in value. • Identifying the quality of data that can be plugged into the analytics algorithm. • Creating data architectures and infrastructures from scratch, including data repositories and analytic environments.• Contributing as a Lead Data Engineer and Analyst for building and developing an innovative analytic product that was readily adopted and provided a competitive advantage for the organization.I build strong relationships within the analytics team. I see our teamwork as an opportunity to provide information that will improve efficiency and empowerment and help the organization be an industry leader. Data engineering software development must be attentive to performance, reliability, and adaptability. I focus on data engineering and developing software-based tools in an analytics team to improve organizational efficiency. My combined education and experience allow me to expertly respond to a wide range of issues, including: Performance - Optimize data pipelines and data services if they run too slowly.Reliability - Prevent disruptions if pipelines or services easily break and are not fault tolerant. Adaptability - Manage change when data sources change, or schema drift disrupts the pipeline.