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.

• 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 (Postgre
  
  SQL) and No
  
  SQL 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.

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

• 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), Postgre
  
  SQL, Redis, Elasticsearch.
• CI/CD & Infrastructure:
  Git, Terraform, Docker; experience with agile methodologies.
• Stream Processing:
  Spark Streaming or similar frameworks.

• Knowledge Graph Technologies:
  Graph DB, OWL, SPARQL.
• Machine Learning Frameworks:
  Tensor Flow, PyTorch, Scikit‑learn, XGBoost.
• Model Deployment:
  Flask, FastAPI, Docker, Kubernetes, Tensor Flow Serving, Torch Serve.
• Exposure to Databricks and workflow orchestration tools.

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