Snowflake Architect

Role: AWS Data Engineer with DS skills

Location: Cambridge, MA (Hybrid- once a week)

Detailed JD:

A hands-on AWS Data Engineer with Data Science skills designs, builds, and maintains scalable cloud data pipelines while operationalizing machine learning models and data science workloads.

Job Description Overview

·       Role: Hands-On AWS Data Engineer with Data Science Skills

·       Core Mission: Bridge the gap between raw data infrastructure and production-ready machine learning by building reliable data lakes, feature stores, and automated ML pipelines on AWS.

Key Responsibilities

·       Data Pipeline & Lakehouse Engineering: Design, build, and optimize end-to-end batch and streaming data pipelines using Amazon S3, AWS Glue, Amazon EMR, and Kinesis.

·       Data Science & ML Integration: Operationalize and deploy machine learning models (scikit-learn, PyTorch, or TensorFlow) into production workflows using SageMaker and Lambda.

·       Feature Engineering & Warehousing: Manage large-scale data storage and analytics using Amazon Redshift and Athena, creating clean feature sets for data science models.

·       Orchestration & Automation: Build robust workflow orchestration using Apache Airflow, MWAA (Managed Workflows for Apache Airflow), or AWS Step Functions.

·       Data Governance & Quality: Ensure data integrity, pipeline monitoring, and security compliance using IAM, Lake Formation, and CloudWatch

Required Qualifications & Skills

·       Cloud & Services: 7+ years of hands-on experience with core AWS big data services (S3, Glue, Redshift, Athena, EMR, SageMaker).

·       Programming: Advanced proficiency in Python and SQL; experience with PySpark or Scala for big data processing.

·       Data Science Acumen: Solid understanding of machine learning lifecycles, feature store concepts, and model deployment/inference patterns.

·       DevOps & Tools: Experience with Docker, Git, CI/CD pipelines, and Infrastructure as Code (Terraform or AWS CDK)