• ATS can't match your profile to job postings
• Recruiters assume lack of expertise

• Analyze job descriptions for core tools (e.g., Spark, Kafka, Airflow)
• Add specific technologies in skills and experience sections
• Use exact product names and versions where relevant

Worked on data pipelines.

Designed and maintained ETL pipelines using Apache Spark 3.2, Kafka, and Airflow for real‑time data processing.

• Hiring managers can't gauge your contribution
• Resume looks generic

• Quantify results (e.g., % improvement, volume processed)
• Use concrete numbers and timeframes
• Show business outcomes

Improved data pipeline performance.

Optimized data pipeline, reducing processing time by 35% and saving $120k annually.

• ATS may misinterpret dates
• Hiring managers struggle to assess timeline

• Use consistent month‑year format (MMM YYYY)
• Place dates on the right side
• Avoid abbreviations like 'Present' without standardization

Jan 2020 – Current

Jan 2020 – Present

• Recruiters lose attention after 2 minutes
• ATS may truncate beyond 2 pages

• Limit to 2 pages for <10 years experience
• Focus on most recent 3‑4 roles
• Remove unrelated projects

Full 5‑page resume with every internship.

Concise 2‑page resume highlighting senior roles and key achievements.

• Data engineering roles often require AWS/GCP/Azure
• ATS filters for cloud certifications

• List cloud services used (e.g., AWS S3, Redshift)
• Include certifications if any
• Show migration projects

Managed data storage.

Implemented data lake on AWS S3 and orchestrated queries with Redshift, achieving 99.9% availability.