Sr Platform Data Engineer.- SGWS

Interview: Virtual (strong candidate - 100 top fortune clients)
Visa: USC and GC
Hybrid: Miramar, FL or Addison, TX
• Must have excellent, clear communication

• Strong AWS: fundamental understanding of cloud trail, cloud watch, S3, and ML platform experience (Glue, Lambda, AWS console management)

• Experienced with both AWS and Redshift

• Knowledge of legacy ETL platforms is good

• Able to look at and know how to migrate over using Databricks/python

• Experience monitoring AWS - Managing Databricks on AWS platform experience integrating with Redshift and Data bricks environment

• 40% development/automation

• 60% management/administration - Databricks, deployment CI/CD pipelines, provisioning projects and pipelines

JD:
We're looking for a Sr. Data Platform Engineer who thrives in a hybrid role-60% administration and 40%
development/support-to help us scale our data and DataOps infrastructure. You'll work with cutting-edge technologies like Databricks, Apache Spark, Delta Lake, and AWS CloudOps, Cloud Security, while supporting mission-critical data pipelines and integrations. If you're a hands-on engineer with strong Python skills, deep AWS experience, and a knack for solving complex data challenges, we want to hear from you.

Responsibilities :

• Design, develop, and maintain scalable ETL pipelines and integration frameworks.
• Administer and optimize Databricks and Apache Spark environments for data engineering workloads.
• Build and manage data workflows using AWS services such as Lambda, Glue, Redshift, SageMaker, and S3.
• Support and troubleshoot DataOps pipelines, ensuring reliability and performance across environments.
• Automate platform operations using Python, PySpark, and infrastructure-as-code tools.
• Collaborate with cross-functional teams to support data ingestion, transformation, and deployment.
• Provide technical leadership and mentorship to junior developers and third-party teams.
• Create and maintain technical documentation and training materials.
• Troubleshoot recurring issues and implement long-term resolutions.

Requirements:

• Bachelor's or Master's degree in Computer Science or related field.
• 5+ years of experience in data engineering or platform administration.
• 3+ years of experience in integration framework development with a strong emphasis on Databricks, AWS, and ETL.
• Strong AWS: fundamental understanding of cloud trail, cloud watch, S3, and ML platform experience (Glue, Lambda, AWS console management)
• Experience managing Databricks on AWS platform and integrating with Redshift and Databricks
• Strong programming skills in Python and PySpark.
• Expertise in Databricks, Apache Spark, and Delta Lake.
• Proficiency in AWS CloudOps, Cloud Security, including configuration, deployment, and monitoring.
• Strong SQL skills and hands-on experience with Amazon Redshift.
• Experience with ETL development, data transformation, and orchestration tools.
• Kafka for real-time data streaming and integration.
• Fivetran and DBT for data ingestion and transformation.
• Familiarity with DataOps practices and open-source data tooling.
• Experience with integration tools such as Apache Camel and MuleSoft.
• Understanding of RESTful APIs, message queuing, and event-driven architectures.