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Protocol Intelligence

Data-driven signals on your job's competitiveness

CV Match Score

Estimated fit based on your resume

Structured overview of role & requirements

5–14 yearsHybridhybridDirect Career PageEst. 12–32 LPA

Against your primary resume

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Role Fit

Experience

Industry Relevance

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Data-driven signals on your job's competitiveness

Competition Intensity

(High)

Mid-level data engineer in metro with generalist title and broad skillset yields high candidate competition.

Background Fit Sensitivity

(Moderate)

GCP-focused data engineering skills are transferable across industries but require cloud-platform expertise.

Shortlisting Strictness

(High)

Explicit 5–14 years plus mandatory GCP, Python, and data pipeline skills enforces strict filters.

Structured overview of role & requirements

Design, develop, and maintain scalable data pipelines and applications on Google Cloud Platform using Python.

Build and manage ETL/ELT processes and optimize data processing workflows for large datasets.

Ensure system reliability, troubleshoot performance issues, and collaborate with engineering, analytics, and business teams.

5 to 14 years of professional experience.

Strong hands-on Python development skills.

Proficiency with Google Cloud Platform services including BigQuery, Cloud Storage, Dataflow, Pub/Sub, Composer, or Dataproc.

Good understanding of SQL and database concepts.

Experienced in building and managing cloud-native data pipelines with scalable architectures on GCP.

Comfortable working cross-functionally with engineering, analytics, and business units to deliver data solutions.

Skilled in performance optimization, monitoring, and applying cloud best practices in data engineering workflows.