-Design and Build Data Processing Systems:

Collaborate with cross-functional teams to understand data requirements and design efficient data pipelines.

Implement data ingestion, transformation, and enrichment processes using GCP services (such as BigQuery, Dataflow, and Pub/Sub).

Ensure scalability, reliability, and performance of data processing workflows.

-Data Ingestion and Processing:

Collect and ingest data from various sources (both batch and real-time) into GCP.

Cleanse, validate, and transform raw data to ensure its quality and consistency.

Optimize data processing for speed and efficiency.

-Data Storage and Management:

Choose appropriate storage solutions (e.g., Bigtable, Cloud Storage) based on data characteristics and access patterns.

Create and manage data warehouses, databases, and data lakes.

Define data retention policies and archival strategies.

-Data Preparation for Analysis:

Prepare data for downstream analytics, reporting, and machine learning.

Collaborate with data scientists and analysts to understand their requirements.

Ensure data is accessible, well-organized, and properly documented.

-Automation and Monitoring:

Automate data workflows using tools like Apache Airflow or Cloud Composer.

Monitor data pipelines, troubleshoot issues, and proactively address bottlenecks.

Implement alerting mechanisms for data anomalies.

-Security and Compliance:

Apply security best practices to protect sensitive data.

Ensure compliance with industry regulations (e.g., GDPR, HIPAA) and internal policies.

Collaborate with security teams to address vulnerabilities.

-Cloud Infrastructure Management:

Manage and monitor Google Cloud Platform (GCP) services and components.

Design and implement automated deployment pipelines for application releases.

Ensure high availability, scalability, and security of cloud resources.

-CI/CD Pipeline Implementation:

Build and maintain continuous integration and continuous deployment (CI/CD) pipelines.

Collaborate with developers to streamline application deployment processes.

Automate testing, deployment, and rollback procedures.

-Infrastructure as Code (IaC):

Use tools like Terraform to define and manage infrastructure.

Maintain version-controlled infrastructure code.

Ensure consistency across development, staging, and production environments.

-Monitoring and Troubleshooting:

Monitor system performance, resource utilization, and application health.

Troubleshoot and resolve issues related to cloud infrastructure and deployment pipelines.

Implement proactive monitoring and alerting mechanisms.

-Security and Compliance:

Apply security best practices to protect cloud resources.

Ensure compliance with industry standards and internal policies.

Collaborate with security teams to address vulnerabilities.

-Documentation and Knowledge Sharing:

Document data pipelines, architecture, and processes.

Share knowledge with team members through documentation, training sessions, and workshops.

-Bachelor’s degree in Computer Science, Information Systems, or a related field.

-Minimum of 3 years of industry experience, including at least 1 year designing and managing solutions using Google Cloud.

-Familiarity with GCP services (BigQuery, Dataflow, Pub/Sub, etc.) and related technologies.

-Experience with data modeling, ETL processes, and data warehousing, SQL

-Familiarity with GCP services (Compute Engine, Kubernetes Engine, Cloud Storage, etc.).

-Exposure to containerization (Docker, Kubernetes) and microservices architecture.

-GenAI prompt engineering, Infra as code, master in CI/CD pipelines, containerization and orchestration, IAM, compliance awareness, design for scalability, cost optimization, multi-cloud and hybrid strategies.

-Proficiency in Python for data manipulation and scripting.

-Strong knowledge of programming languages such as Java

-Knowledge of Terraform for infrastructure as code (IaC).

-Familiarity with Jenkins for continuous integration and deployment.

-Strong understanding of DevOps principles and practices

-Experience with GKE (Google Kubernetes Engine) for container orchestration.

-Understanding of event streaming platforms (e.g., Kafka, Google Cloud Pub/Sub).

-Strong problem-solving skills and attention to detail.

-Proven experience in AI and machine learning, preferably in the banking or financial services industry