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Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable ETL/ELT data pipelines and architectures using AWS services to enable reliable data movement and analytics.
Develop and optimize Spark and PySpark jobs for batch and real-time data processing within a large-scale data ecosystem.
Collaborate with cross-functional teams to support data availability, quality, governance, and deployment automation for reporting and insights used by millions of users.
Minimum Requirements
Bachelor's degree in Computer Science, Information Technology, Data Engineering, or related field.
3-5 years of hands-on experience in data engineering, pipeline development, or cloud-based data systems.
Strong skills in SQL and Python/PySpark.
Practical experience with AWS data services including S3, Glue, Lambda, Redshift, Athena, EMR, and Step Functions.
Ideal Candidate Profile
Experienced with Data Lake architecture, ETL/ELT frameworks, and data warehousing concepts to optimize data pipelines.
Comfortable working with big data frameworks such as Delta Lake, Spark SQL, and performing data modeling and performance tuning.
Able to manage code and deployment through Git and CI/CD tools within a collaborative engineering environment.