Back

Protocol Intelligence

Data-driven signals on your job's competitiveness

CV Match Score

Estimated fit based on your resume

Structured overview of role & requirements

6–10 yearsDirect Career PageEst. 15–28 LPA

Posted 21h ago

Against your primary resume

Create a free account or log in to unlock your CV match score across:

Role Fit

Experience

Industry Relevance

get started

Data-driven signals on your job's competitiveness

Competition Intensity

(High)

Background Fit Sensitivity

(Moderate)

Shortlisting Strictness

(High)

Structured overview of role & requirements

Design, develop, and maintain scalable ETL/ELT pipelines using Python and PySpark for large volumes of structured and unstructured data.

Optimize data processing workflows and Spark jobs for performance, scalability, and cost efficiency in cloud environments (AWS, Azure, GCP).

Implement data ingestion, cleansing, validation, transformation, quality checks, and monitor data pipelines with alerting mechanisms.

6-10 years of relevant work experience in data engineering.

Strong proficiency in Python, PySpark, and complex SQL development including stored procedures and views.

Experience with cloud-native data services on AWS, Azure, or GCP.

Onsite work location mandatory: Bengaluru or Hyderabad.

Experienced in handling large-scale data processing workflows and optimizing Spark jobs within cloud platforms.

Able to collaborate effectively with business stakeholders, analysts, and architects to translate data requirements into technical solutions.

Familiar with Agile development practices, CI/CD pipelines, version control (Git), and production troubleshooting in data engineering contexts.