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Job Description: • Develop, maintain scalable data pipelines and build out new integrations to support continuing increases in data volume and complexity • Develop and maintain scalable, optimized data pipelines leveraging Python and AWS services to support increasing data volume and complexity, while ensuring seamless integration with AI platforms like Bedrock and Google • Further enhance data accessibility and drive data-driven decision making by collaborating with analytics and business teams to refine data models for business intelligence tools • Develop data models, schemas, and standards that ensure data integrity, quality, and accessibility • Develop, maintain, and optimize scalable data pipelines using Python and AWS services (e.g., S3, Lambda, ECS, EKS, RDS, SNS/SQS, Vector DB) • Build solutions with AI Services like Bedrock, Google etc • Rapidly developing next-generation scalable, flexible, and high-performance data pipelines • Collaborate with analytics and business teams to create and improve data models for business intelligence • End-to-end ownership of data quality in our core datasets and data pipelines • Participate in code reviews and contribute to DevOps / DataOps / MLOps Job Requirements: • Bachelor's degree in Computer Science, Engineering, or a related field • 5-6 years of experience in data engineering or a similar role • Strong programming skills in Python, SQL, AWS and related tech stack • Experience with building scalable data pipelines with technologies such as Glue, Airflow, Kafka, Spark etc. • Experience using Snowflake, DBT, Bedrock is a plus • Good understanding of basic machine learning concepts (Sagemaker)