Overview
Skills
Job Details
Role Name: Google Cloud Platform Data Engineer
Location: Philadelphia, PA (Hybrid, 3 days/week onsite)
Duration: 6+ Months
Job Overview: Client is seeking a talented and experienced Data Engineer to design and implement scalable cloud-native data solutions. This role will involve crafting data platforms, optimizing pipelines, and integrating machine learning workflows for real-time analytics and operational reporting. The position requires strong expertise in Google Cloud Platform (Google Cloud Platform), distributed computing frameworks, and CI/CD infrastructure.
Key Responsibilities: Data Platform Development:
- Design and implement scalable data storage and processing architectures.
- Develop cloud-native systems enabling real-time processing and analytics.
- Optimize data models, schemas, and indexing strategies for performance and cost efficiency.
ETL & Data Pipeline Optimization:
- Build efficient ETL pipelines using SQL and Python.
- Develop streaming solutions leveraging Kafka or Apache Beam.
- Enhance automation for data validation, governance, and monitoring dashboards.
MLOps Integration & Infrastructure Automation:
- Deploy machine learning pipelines with MLOps best practices.
- Leverage tools like Terraform and Jenkins to automate deployments and manage data infrastructure.
- Utilize Google Cloud Platform tools such as GitHub, Docker, and Kubernetes for seamless operations.
Collaboration:
- Work with cross-functional Agile teams to develop data-driven solutions.
- Provide guidance on best practices in data engineering and architecture.
Required Skills:
- 5+ years of experience as a Data Engineer handling large-scale data processing.
- Proficiency in SQL for data transformation and analytics.
- Expertise in programming languages (Python, Java, Scala, or Go).
- Knowledge of distributed computing and cloud platforms (AWS, Google Cloud Platform, Azure).
- Familiarity with CI/CD tools like Jenkins and Terraform.
Preferred Skills:
- Experience with real-time data streaming (Kafka or similar).
- Knowledge of MLOps and ML pipeline optimization.
- Background in retail and customer classification systems.
- Familiarity with knowledge graphs, GraphQL APIs, and BI visualization tools.
Best Regards,
Vishal
Truth Lies in Heart