September 18, 2024
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Plano, TX, United States
September 18, 2024
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What We Are Looking For
Highly experienced Data Engineer with expertise in cloud engineering, data engineering, and data warehousing, particularly within the AWS and google cloud ecosystem. Success in designing and implementing scalable cloud solutions, data pipelines, optimizing data architectures, and delivering high-quality analytics solutions. Proficient at collaborating with cross-functional teams to drive business insights and data-driven decision-making. Skilled in automating processes and ensuring high availability and security in cloud environments.
Cloud Platforms: AWS (EC2, S3, RDS, Lambda, CloudFormation, VPC, IAM, Redshift)
Data Engineering: ETL, Data Warehousing, Data Modeling, SQL, NoSQL
Big Data Technologies: Hadoop, Spark, Athena, materialized views
DevOps Tools: Docker, Kubernetes, Jenkins, Terraform, Git, Big query, Firebase
Workflow Orchestration: Apache Airflow
Programming Languages: Python, SQL, Java, Go, Bash
Data Visualization: Tableau, Looker, Power BI, Quicksight
Monitoring & Logging: CloudWatch, Prometheus, Grafana, ELK Stack
Other Tools: GitLab CI/CD
Security: ForgeRock, Keycloak, SSO solutions for data platforms
Highly experienced Data Engineer with expertise in cloud engineering, data engineering, and data warehousing, particularly within the AWS and google cloud ecosystem. Success in designing and implementing scalable cloud solutions, data pipelines, optimizing data architectures, and delivering high-quality analytics solutions. Proficient at collaborating with cross-functional teams to drive business insights and data-driven decision-making. Skilled in automating processes and ensuring high availability and security in cloud environments.
- Implemented scalable cloud architectures for enterprise applications using AWS services for Data platforms
- Develops and maintains CI/CD pipelines to automate deployment processes and ensure continuous integration.
- Leads teams in designing and developing centralized data warehouses and data lakes. Preferably using redshift
- Creates and optimizes ETL processes to ingest and transform large datasets for analytical purposes.
- Utilizes Apache Airflow for scheduling and monitoring data workflows to ensure timely data delivery.
- Implements data partitioning, compression strategies, and query optimization techniques.
- Develops serverless applications using AWS Lambda and API Gateway to reduce operational costs.
- Automates configuration management using tools like Ansible and Terraform.
- Conducts security assessments and implemented IAM and VPC best practices to secure cloud environments.
- Integrated monitoring and logging solutions using CloudWatch, Prometheus, and ELK Stack for performance and security monitoring.
- Created automated reporting solutions and interactive dashboards using Tableau, Looker, and Power BI.
- Conducts data quality checks and ensured data integrity across multiple data pipelines.
- Ability to securely ingest data from public and other Toyota sources to better insights
Cloud Platforms: AWS (EC2, S3, RDS, Lambda, CloudFormation, VPC, IAM, Redshift)
Data Engineering: ETL, Data Warehousing, Data Modeling, SQL, NoSQL
Big Data Technologies: Hadoop, Spark, Athena, materialized views
DevOps Tools: Docker, Kubernetes, Jenkins, Terraform, Git, Big query, Firebase
Workflow Orchestration: Apache Airflow
Programming Languages: Python, SQL, Java, Go, Bash
Data Visualization: Tableau, Looker, Power BI, Quicksight
Monitoring & Logging: CloudWatch, Prometheus, Grafana, ELK Stack
Other Tools: GitLab CI/CD
Security: ForgeRock, Keycloak, SSO solutions for data platforms
