Data Engineer - AWS, Bedrock

Data Engineer specializing in AWS Bedrock Experience: 7+ Years Location: Chicago, IL 3 days/week What is in it for you?

A highly skilled Data Engineer specializing in AWS Bedrock and modern data platforms, responsible for designing, building, and optimizing scalable data solutions and pipelines for advanced analytics and AI-driven applications.

Responsibilities:

• Design, develop, and maintain robust data pipelines and architecture for large-scale data processing.
• Implement and optimize data workflows using AWS services (Glue, Lambda, EMR, Kinesis) and Bedrock.
• Collaborate with data scientists and ML engineers to integrate machine learning models into production environments.
• Ensure data quality, security, and compliance across all stages of the data lifecycle.
• Develop CI/CD pipelines for data engineering projects using Git, Terraform, and containerization tools.
• Work with streaming and batch processing frameworks (Spark, Kafka/Kinesis, Spark Streaming).
• Manage and optimize relational (PostgreSQL) and NoSQL databases (Redis, Elasticsearch).
• Monitor and troubleshoot data systems for performance and reliability.
• Stay updated on emerging technologies in big data, AI, and cloud platforms.
• Collaborate closely with teams in an Agile/Scrum environment.

Educational Qualifications:

• Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
• Technical certification in multiple technologies is desirable.

Skills: Mandatory Skills:

• Programming: Strong proficiency in Python; ability to learn other languages quickly.
• AWS Expertise: Hands-on experience with AWS Bedrock, Lambda, Glue, Athena, Kinesis, IAM, EMR/PySpark.
• Big Data Technologies: EMR, Spark, Kafka/Kinesis, Airflow.
• Databases: Advanced SQL (complex queries), PostgreSQL, Redis, Elasticsearch.
• CI/CD & Infrastructure: Git, Terraform, Docker; experience with agile methodologies.
• Stream Processing: Spark Streaming or similar frameworks.

Good to Have Skills:

• Knowledge Graph Technologies: Graph DB, OWL, SPARQL.
• Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost.
• Model Deployment: Flask, FastAPI, Docker, Kubernetes, TensorFlow Serving, TorchServe.
• Exposure to Databricks and workflow orchestration tools.