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

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).

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Operating Division

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