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# AWS Data Engineer with Data Scientist skills

- **Company:** Peoplentech Llc
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $153,920.0 - $158,080.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Amazon S3, Big Data, Cloud Computing, Computer Programming, Continuous Integration, Data Governance, Data Integrity, DevOps, Identity and Access Management, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Tensorflow, SQL Databases, Data Streaming, AWS Cdk, Feature Engineering, Pytorch, Git, Pyspark, Infrastructure Automation Frameworks, AWS Lake Formation, AWS Glue, AWS Data Analytics, Data Management, Machine Learning Operations, Cloudwatch, Terraform, Data Pipelines, Amazon Elastic Mapreduce (EMR), Docker, Amazon Redshift
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/aws-data-engineer-with-data-scientist-skills-santa-clara-ca--c09c02f3-0242-48a1-9a1d-2bd4ab637a34

## About the Role

Amazon Web Services (AWS), Data Science, Machine Learning, Data Science, Data Management, Amazon Web Services (AWS), Cloud Computing, Data Modeling, Scalable System Development, Production Machining, · 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)

## Description

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., · 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