AWS Data Engineer
Microgreen Technologies Llc
Plano, TX, United States
2 days ago
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Role details
Contract type
Contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
Tech stack
Java (Programming Language)
Application Programming Interfaces (APIs)
Artificial Intelligence
Amazon Web Services
Amazon Elastic Compute Cloud
Amazon S3
Apache HTTP Server
Microsoft Azure
Big Data
Computer Programming
Databases
Continuous Integration
+33 more
Information Engineering
Data Integration
DevOps
Amazon DynamoDB
Python (Programming Language)
PostgreSQL
Microsoft Copilot
Systems Integration
Data Processing
Scripting
Google Cloud
Data Ingestion
Snowflake
Apache Spark
Boto3
Agentic-AI
Git
Data Lakes
Pyspark
Deployment Automation
AWS Glue
AWS Data Analytics
Bitbucket
Claude
Api Design
Cloudwatch
Terraform
Splunk
Data Pipelines
Jenkins
Servicenow
Databricks
Control M
Job description
- Design, develop, and maintain scalable AWS-based data pipelines.
- Build data ingestion and transformation workflows using AWS Glue, Lambda, and Step Functions.
- Develop Python scripts, APIs, and automation using Boto3.
- Implement and manage AWS data lake solutions using S3 and Lake Formation.
- Work with PostgreSQL, DynamoDB, Spark, and PySpark for data processing.
- Implement CI/CD pipelines and automate deployments using Jenkins, Bitbucket, and Terraform.
- Monitor data pipelines and troubleshoot issues using CloudWatch and Splunk.
- Collaborate with cross-functional teams to deliver reliable data integration solutions.
Requirements
We are seeking a hands-on AWS Data Engineer with strong experience in AWS data services, Python development, and end-to-end data pipeline development. The ideal candidate should have experience building production-grade data pipelines from scratch, working with AWS data lakes, databases, and CI/CD deployments.
Must-Have Technical Skills
- AWS Data Services: Strong hands-on experience with S3, Glue, Lambda, Step Functions, and Lake Formation.
- Python: Strong Python programming, scripting, API development, and Boto3.
- Data Engineering: Experience designing and developing end-to-end data pipelines, including data ingestion, transformation, and integration.
- Databases: Strong knowledge of PostgreSQL and DynamoDB.
- Big Data: Hands-on experience with Apache Spark and PySpark.
- AWS Data Lake: Experience developing and managing AWS-based data lake solutions.
- CI/CD & DevOps: Experience with Jenkins, Bitbucket/Git, Terraform, and automated deployments.
- Monitoring: Experience with AWS CloudWatch and Splunk.
- Additional Tools: Familiarity with ServiceNow and enterprise deployment processes.
- AI Exposure: Basic understanding of Copilot, Claude, AI agents, and agentic workflows.
Good-to-Have Skills
- Snowflake, including Horizon Catalog.
- Databricks and Unity Catalog.
- Apache Iceberg.
- AWS Athena, Redshift, EMR, and S3 Select.
- AWS EC2 and Control-M scheduling.
- AWS Bedrock and AI application integrations.
- Exposure to Google Cloud or Microsoft Azure.
- Java/Scala programming knowledge., * Strong hands-on AWS Data Engineering and Python development experience.
- Proven ability to build data pipelines from scratch.
- Strong understanding of AWS data lake architecture and database technologies.
- Developers preferred over candidates focused primarily on support, maintenance, or architecture.
- Ability to work independently in a production environment.
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