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Popular mid-level data role with broad in-demand tech requirements increases applicant competition.
Core data engineering skills are highly transferable across industries.
Explicit 6–8 year requirement and multiple mandatory technologies make shortlisting highly stringent.
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Estimate, analyze, develop, and maintain ETL data pipelines for ingestion, transformation, and loading into data repositories.
Create and maintain logical and physical data models aligned with organizational data architecture and business needs.
Lead technical discussions, optimize pipeline performance and costs, monitor operations, and implement automation and reusable frameworks.
6-8 years hands-on experience with AWS services including S3, Lambda, Glue, Athena, RDS, Step Functions, SNS, SQS, API Gateway, security, and monitoring.
Strong expertise in Python, Spark, AWS CLI, Snowflake DBT, and streaming solutions like Kafka.
Experience with multiple databases such as MySQL and Oracle with ability to write complex queries.
Work Experience Required: 6-8 years in relevant data engineering roles.
Experienced data engineer with strong background in AWS cloud services and Snowflake data platform.
Comfortable leading technical discussions and collaborating with cross-functional Agile teams using Scrum/Kanban.
Capable of designing data models, optimizing data pipelines, and implementing automation in CI/CD environments.