Data Engineer with 10+ years in scalable ETL pipelines, cloud-native data platforms, and AWS infrast
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Engineer with 10 years of experience designing scalable ETL pipelines, cloud-native data platforms, and production-grade transformation workflows across finance, healthcare, and technology sectors. Proven ability to deliver reliable Snowflake-based architectures, orchestrate workflows with Airflow, and manage secure AWS infrastructure for high-volume ingestion. Consistently bridges technical execution with business outcomes — reducing pipeline failures, accelerating reporting readiness, and enabling teams to trust their data.
University of Texas at Austin
Bachelor's Degree · Computer Science
N/A – June 30, 2014
Teradata
Data Engineer
September 1, 2022 – Present
San Diego, California, United States
New Relic
Data Engineer
January 1, 2018 – August 1, 2022
San Francisco, California, United States
Black Box Intelligence
Software Engineer
September 1, 2014 – January 1, 2018
Dallas, Texas, United States
Microsoft
Software Engineering Intern
June 1, 2014 – August 1, 2014
Austin, Texas, United States
Cultural Fit Analysis
The candidate's diverse project experience across multiple industries (Healthcare, Finance, Telecom, Retail, Technology) and their involvement in mentoring and establishing global standards suggest a strong cultural fit for collaborative and impactful roles. Their focus on improving engineering consistency, reducing escalations, and partnering with various teams (product, security, SRE) aligns with a culture that values teamwork, continuous improvement, and cross-functional collaboration. The breadth of their technical skills also indicates adaptability and a willingness to learn and apply new technologies.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through their experience in automating workflows, building observability dashboards, creating runbooks, and mentoring junior engineers. Their ability to lead PoCs and establish architectural standards indicates strong problem-solving, leadership, and collaboration skills. The detailed descriptions of their contributions, including quantifiable improvements, suggest excellent communication and a results-oriented mindset.