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Metro location and common AWS/Spark/ETL requirements make applicant competition moderate.
Core AWS, Spark and ETL skills transfer well across industries, so background sensitivity is moderate.
Mandatory 7+ years plus specific AWS, Spark, ETL and BI requirements increase filtering rigor.
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Design and support analytical data infrastructure enabling ad-hoc access to large datasets and computing resources.
Develop and maintain real-time data pipelines and ETL processes using AWS big data technologies (Glue, Redshift, Kinesis, EMR, Athena).
Collaborate with technical teams to implement advanced analytics and automate reporting to enhance data accessibility and operational efficiency.
7+ years experience in software development, data engineering, business intelligence, or related field handling large datasets.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or related technical discipline.
Proven skills in data modeling, ETL development, data warehousing, and big data processing with Spark and AWS technologies (Redshift, S3, EMR).
Experience building and operating distributed data extraction, ingestion, and processing systems; knowledge of software engineering best practices including agile, testing, and version control.
Strong proficiency in building and maintaining scalable, distributed data systems with AWS ecosystem components.
Experienced in implementing real-time data processing and analytics pipelines supporting business-critical risk monitoring.
Able to bridge data engineering with analytics by collaborating on advanced analytics, machine learning implementations, and BI reporting automation.