Job Specifications
Role: “AWS Cloud Engineer”
Location: Seattle, WA / St. Louis, MO / Plano, TX / Dallas, TX / Houston, TX (Onsite from Day 1)
Position: Fulltime
Job Description:
AWS data services (S3, Glue, Redshift, Athena, Lambda, Step Functions, Kinesis, etc.).
Unity Catalog, Pyspark, AWS Glue, Lambda, Step Functions, and Apache Airflow
AWS data services (S3, Glue, Redshift, Athena, Lambda, Step Functions, Kinesis, etc.)
Programming skills in Python, Scala, or Pyspark for data processing and automation
Expertise in SQL and experience with relational and NoSQL databases (e.g., RDS, DynamoDB).
Data Pipeline Development: Design, develop, and optimize ETL/ELT pipelines using AWS & Databricks services such as Unity Catalog, Pyspark, AWS Glue, Lambda, Step Functions, and Apache Airflow.
Data Integration: Integrate data from various sources, including relational databases, APIs, and streaming data, ensuring high data quality and consistency.
Cloud Infrastructure Management: Build and manage scalable, secure, and cost-efficient data infrastructure using AWS services like S3, Redshift, Athena, and RDS.
Data Modeling: Create and maintain data models to support analytics and reporting requirements, ensuring efficient querying and storage.
Performance Optimization: Monitor and optimize the performance of data pipelines, databases, and queries to meet SLAs and reduce costs.
Collaboration: Work closely with data scientists, analysts, and software engineers to understand data needs and deliver solutions that enable business insights.
Security and Compliance: Implement best practices for data security, encryption, and compliance with regulations such as GDPR, CCPA, or ITAR.
Automation: Automate repetitive tasks and processes using scripting (Python, Bash) and Infrastructure as Code (e.g., Terraform, AWS CloudFormation).
Agile Development: Build and optimize continuous integration and continuous deployment (CI/CD) pipelines to enable rapid and reliable software releases using Gitlab in an Agile environment.
Monitoring and Troubleshooting: Set up monitoring and alerting for data pipelines and infrastructure, and troubleshoot issues to ensure high availability.