Data & AI

Is Your Senior Data Engineer Resume
Failing the ATS Scan?

Big Tech ATS filters reject most Senior Data Engineer resumes before a human ever reads them. We built the diagnostic rubric used by Google, Meta, and Amazon recruiters.

22%
Avg. Interview Rate
Impact > Keywords
72h
ATS Review Window

The Senior Data Engineer Hiring Rubric

Model Impact
35%

Measurable improvements tied to model decisions: precision lift, latency reduction, or revenue attributed. 'Built ML models' without business context scores near zero.

Data Scale
25%

Pipeline throughput, dataset size, and query latency. Big Tech benchmarks: trillions of events, sub-100ms SLAs, petabyte-scale processing. Volume signals seniority.

Technical Rigor
25%

Named ML frameworks (PyTorch, TensorFlow, XGBoost), evaluation methodologies, and experiment design. Vague 'machine learning' without specifics scores low.

Business Framing
15%

How model outputs connect to business outcomes — revenue, retention, engagement lift. The weakest signal on most ML resumes, and the hardest to fake.

Top Keywords You're Likely Missing

data mesh architecturestreaming pipelinesdata contractslakehouse designpipeline observability

Our analysis of 10,000+ Senior Data Engineer applications shows these are the most common gaps between rejected and shortlisted candidates at Big Tech companies.

Sample Audit Report

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