Senior Data Engineer – Python, PySpark & AWS
Required skills
- Python
- PySpark
- AWS
- S3
- EMR
- Lambda
- Terraform
- REST APIs
- Kafka
- MQ messaging
Nice to have
- Databricks
- machine-learning workflows
Role summary
Seeking a Senior Data Engineer with strong Python, PySpark, and AWS experience to build and maintain scalable data pipelines and distributed data processing solutions. Responsibilities include developing ETL/ELT pipelines, supporting big-data solutions on AWS (S3, EMR, Lambda), implementing infrastructure automation with Terraform, developing data integrations via REST APIs, Kafka, and MQ messaging, supporting data storage with Cassandra, and orchestrating workflows with Airflow or Control-M. The role requires troubleshooting pipeline failures and optimizing performance, reliability, and data quality. Experience with Databricks and machine-learning workflows is preferred.
Senior
Data Engineer – Python, PySpark & AWS
Location:
Plano, TX
Relocation
: Open
Open for W2 only- No c2c/1099 or third party resumes
Due to the nature of the roles client is only
accepting USC/GC /GC EAD only no other visas accepted.
Role
Overview
We are
seeking a hands-on Senior Data Engineer with strong Python, PySpark, and AWS
experience to develop and maintain scalable data pipelines and distributed data
processing solutions. The ideal candidate will have experience supporting
production data engineering workloads, cloud infrastructure, and data
orchestration.
Key
Responsibilities
- Develop, test, and maintain ETL/ELT data pipelines using Python and PySpark.
- Build and support big-data processing solutions on AWS using S3, EMR, and Lambda.
- Implement infrastructure automation using Terraform.
- Develop data integrations using REST APIs, Kafka, and MQ messaging.
- Support data storage and retrieval using Cassandra.
- Orchestrate and monitor workflows using Airflow, Control-M, and native schedulers.
- Troubleshoot pipeline failures and optimize performance, reliability, and data quality.
Required
Skills
- Strong hands-on Python and PySpark experience.
- Experience with big-data processing and production data pipelines.
- AWS experience with S3, EMR, and Lambda.
- Working knowledge of Terraform.
- Familiarity with Kafka, MQ messaging, REST APIs, Java, and Cassandra.
- Experience with Airflow, Control-M, or other workflow scheduling tools.
Preferred: Databricks experience and exposure
to machine-learning workflows.
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