Data Engineer (AWS+Pyspark)
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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level popular Data Engineer role in a metro with common AWS/PySpark skills increases competition.
AWS, PySpark and SQL skills are highly transferable across industries, lowering background sensitivity.
Explicit 4–7 years plus mandatory AWS, PySpark, SQL and IAM skills create strict filtering.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and support end-to-end data pipelines leveraging AWS services and big data frameworks.
Optimize and maintain complex data processing workflows using Python, PySpark, and SQL for large datasets.
Manage AWS infrastructure components including IAM roles, policies, and monitoring with CloudWatch and CloudTrail for secure and reliable data operations.
Minimum Requirements
4–7 years of hands-on experience as a Data Engineer focused on AWS data pipelines.
Strong programming skills in Python and PySpark with big data processing experience.
Expertise in AWS core services including Lambda, RDS, CloudWatch, CloudTrail, SNS, SQS and analytics services like EMR, Glue, Lake Formation, DynamoDB.
Advanced SQL skills for complex queries and performance tuning on large datasets.
Ideal Candidate Profile
Experienced in secure AWS infrastructure management with emphasis on IAM role design and least-privilege policy enforcement.
Proven track record of building resilient, scalable data ingestion and transformation pipelines in distributed environments.
Strong operational focus on monitoring, troubleshooting, and root cause analysis of data workflows and AWS service integrations.