
Senior GCP Data Specialist
2 weeks ago
Job Description
">- We are seeking a skilled data engineer to design, develop, and maintain scalable data pipelines using GCP tools.
- The successful candidate will be responsible for constructing large datasets, translating business requirements into technical specifications, and leveraging various GCP services to build integrated systems.
- Experience with Cloud Dataflow (Apache Beam), Big Query, Cloud Run, Cloud Functions, and Pub/Sub is essential.
- The ideal candidate will have a strong understanding of different file formats, knowledge of data lake concepts, and experience with API development and consumption.
Required Skills and Qualifications
- Proficiency in Python and SQL is required.
- Experience with structured ERP solutions like SAP, Oracle EBS, and Salesforce is beneficial.
- A strong understanding of data security and governance principles is necessary.
Benefits
- This is a remote opportunity for a specialist with 10+ years of experience to work on challenging projects for industries like Energy, Oil & Gas, Healthcare, and Finance.
- As a GCP Data Engineer, you will be at the forefront of our data solutions, working closely with the team to design and implement robust data pipelines.
About This Role
- Designing, developing, and maintaining scalable data pipelines for both batch and real-time processing using GCP tools.
- Constructing large, complex datasets to meet diverse business needs.
- Translating business requirements into clear technical specifications.
- Leveraging a variety of GCP services, including Big Query, Cloud Dataflow (Apache Beam), Cloud Run, Cloud Functions, and Pub/Sub to build integrated systems.
- Orchestrating workflows using Cloud Composer (Apache Airflow).
- Working with SAP SLT to replicate SAP data to Google Cloud.
- Implementing DevOps and CI/CD practices (GitHub, Terraform) to ensure the reliability and scalability of data pipelines.
- Ensuring data security and governance using IAM roles, policies, and data masking techniques.
- Monitoring pipelines using logs and alerts to proactively manage performance.
- Recommending improvements to enhance data quality, reliability, and efficiency.
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