Overview
On Site
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 6 month(s)
Skills
Kubernetes
Cloud Storage
PySpark
Python
Apache Spark
Continuous Integration
Continuous Delivery
Jenkins
GitHub
SQL
Physical Data Model
Extract
Transform
Load
Reporting
Analytics
Functional Requirements
Design Patterns
Business Rules
Collaboration
Pharmacy
Orchestration
Cloud Computing
Data Flow
Workflow
Pricing
Real-time
Optimization
Google Cloud Platform
Google Cloud
Data Quality
Job Details
Tittle : Google Cloud Platform Data Engineer
Location: Richardson Dallas TX (Day 1 onsite)
Job Description
Need 12 + years of experience , PySpark, Python,proactive monitoring and alert mechanism , datacore
- Experience with Google Cloud Platform services such as Compute Engine, Data Proc, Kubernetes Engine, Cloud Storage, BigQuery, PUB/SUB, Cloud Functions and Dataflow.
- Cloud Composer, ETL experience - working with large data sets, PySpark, Python, Spark SQL, DataFrames, PyTest
- Develop and implement proactive monitoring and alert mechanism for data issues.
- Familiarity with CI/CD pipelines and automation tools such as Jenkins, GitHub & GitHub actions.
- Able to write complex SQL queries for business results computation
- Develop architecture recommendations based on Google Cloud Platform best practices and industry standards.
- Work through all stages of a data solution life cycle: analyze/profile data, create conceptual, logical & physical data model designs, architect and design ETL, reporting, and analytics solutions.
- Conduct technical reviews and ensure that Google Cloud Platform solutions meet functional and non-functional requirements.
- Strong knowledge of Google Cloud Platform architecture and design pattern
- Business Logic & Workload Processing Data Engineer Responsibilities
- Developing Workloads for Business Logic Execution
Designed and implemented scalable workloads in Google Cloud Platform (Google Cloud Platform) to process complex business rules for Rx claim pricing and drug coverage analysis. - Business Rule Integration
Collaborated with stakeholders to translate regulatory and business requirements into executable rules. These rules determine drug pricing based on plan configurations, drug coverage, and pharmacy-specific factors. - Workload Orchestration
Leveraged Cloud Composer, Dataflow, and BigQuery to build automated, efficient workflows that:- Ingest and validate data
- Apply rule-based logic at scale
- Output regulatory-compliant pricing files
- Dynamic Rule Processing
Supported rule versioning and updates to ensure accurate real-time processing across large f data. - Optimization & Monitoring
Tuned workload performance for cost efficiency and monitored execution through Google Cloud Platform's built-in tools, ensuring timely delivery and data quality
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