Data Engineer

Martin Smith

Professional Summary

Data Engineer with hands-on experience in building scalable, cloud-native data pipelines on Google Cloud Platform (GCP). Skilled in SQL, Python, and PySpark, with a strong focus on data quality, performance optimization, and business-driven analytics. Proven ability to deliver clean, reliable pipelines using BigQuery, Dataflow, and Cloud Composer.

Work Experience

Data Engineer at Acme Corp

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Built a scalable pipeline with Dataflow and Cloud Composer to unify a Fortune 500 retailer’s supply chain data in BigQuery. Tuned BigQuery queries to support 10x faster reporting for demand forecasting. Helped reduce inventory holding costs by 15–30% through better insights and availability tracking.

Software Developer Intern at Acme Startup

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Developed frontend dashboards using React.js, consuming SQL-based APIs. Collaborated with cross-functional teams using Git and Jira in an agile setup.

AI Engineering Intern at Acme Biz

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Built and deployed an AI-powered FinTech app using React.js, Java, and machine learning models.

Education

B.Tech in Computer Science

University of Waterloo

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Skills

Technical Skills

  • Data Pipeline Development
  • BigQuery
  • Dataflow
  • Cloud Storage
  • Pub/Sub
  • Cloud Composer
  • Python
  • SQL
  • Git
Score
82/100
Work Experience
78/100
Header
85/100
Skills
75/100
Certifications
0/100
Projects
80/100
Education
70/100
Profile
80/100
Add certifications relevant to data engineering and cloud technologies to boost credibility and ATS ranking.
Use consistent formatting with bullet points and date styles across sections to improve readability and ATS parsing.
Expand soft skills and tools sections to provide a fuller candidate profile.
Include more quantifiable achievements in profile and earlier work experiences.
Add dates to projects and more detailed descriptions to highlight scope and impact.
Strong hands-on experience with Google Cloud Platform services relevant to data engineering.
Clear demonstration of measurable impact in work experience, such as cost reduction and performance improvements.
Relevant and up-to-date technical skills aligned with industry demands.
Projects showcase end-to-end pipeline development and real-world application of skills.

Work Experience

78%
Descriptions include relevant technologies and measurable outcomes, which enhance recruiter appeal.
Use consistent formatting for dates and employment types to improve readability and ATS parsing.
Expand on earlier roles with more technical details or accomplishments to show career progression and depth.
Consider adding bullet points rather than paragraphs for easier scanning by ATS and human readers.

Header

85%
Header includes essential contact information and professional headline, which is good for ATS parsing and recruiter clarity.
Consider adding a professional title keyword like 'Data Engineer' explicitly in the name line or as a title for stronger ATS keyword recognition.

Skills

75%
Hard skills are well aligned with the data engineering domain and include key cloud and programming technologies.
Add soft skills and tools to provide a more rounded skill profile that ATS systems often look for.
Consider grouping skills by category (e.g., Programming Languages, Cloud Platforms, Data Tools) for clarity.

Certifications

0%
Section missing

Projects

80%
Projects demonstrate practical application of data engineering skills with specific technologies and outcomes.
Include dates or durations for projects to provide temporal context.
Use bullet points to break down project tasks and results, enhancing readability.
Add metrics or impact statements where possible to quantify success.

Education

70%
Basic education details are present with institution and degree, which is sufficient for ATS.
Include graduation month if possible to improve timeline clarity.
Add any relevant coursework, projects, or honors to strengthen this section.

Profile

80%
Summary is concise and highlights key skills and technologies relevant to data engineering roles.
Add a few quantifiable achievements or metrics in the profile to immediately demonstrate impact.
Including a brief objective or career goal could help tailor the profile more explicitly to targeted roles.
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