Snowflake Developer with Oracle Golden Gate/ Data Engineer

Job Overview:

We are looking for an experienced Snowflake Developer / Data Engineer to join our growing data engineering team. This role requires expertise in Snowflake architecture, data replication with Oracle GoldenGate for Big Data, real-time data ingestion using Snowpipe, and the ability to design and implement robust data pipelines and data models. Knowledge of AWS cloud services, CI/CD, and familiarity with Databricks are also key to success in this role. If you are passionate about data, cloud technologies, and working in an innovative environment, we encourage you to apply.

Responsibilities:

• Snowflake Data Modeling & Architecture:
  Design, develop, and implement scalable and high-performing Snowflake data models and solutions tailored to meet business and data analytics needs. Implement best practices for Snowflake Data Vault methodology and other Snowflake-specific features.
• Real-Time Data Replication & Ingestion:
  Manage and optimize data replication using Oracle GoldenGate for Big Data to facilitate real-time data streaming and transformation into Snowflake. Leverage Snowpipe for efficient and automated data ingestion from various sources into Snowflake.
• Cloud Integration & Management:
  Work with AWS cloud services (e.g., S3, EC2, Lambda) to manage and deploy Snowflake-based data solutions, ensuring smooth integration of cloud resources with Snowflake.
• Data Sharing & Security:
  Utilize SnowShare to securely share Snowflake data across teams and external partners. Implement robust data security measures, including role-based access control (RBAC), data masking, and encryption, to ensure compliance with industry standards.
• CI/CD Implementation:
  Implement and manage CI/CD pipelines for Snowflake development and deployment processes, streamlining the delivery of new data models, transformations, and features.
• Collaboration & Troubleshooting:
  Collaborate with cross-functional teams (data analysts, data scientists, business intelligence teams) to understand data requirements and troubleshoot any data-related issues within Snowflake. Provide support in the resolution of data pipeline challenges.
• Documentation & Best Practices:
  Develop and maintain comprehensive documentation for data architecture, data flows, ETL processes, and Snowflake configurations to ensure knowledge sharing and alignment across the team. Stay updated on emerging trends and best practices in data engineering and Snowflake technologies.
• Performance Optimization:
  Continuously optimize the performance of data pipelines, queries, and overall Snowflake architecture for better efficiency and reduced costs.

Requirements:

• Experience:
  4 years of experience as a Data Engineer or Snowflake Developer, with a focus on Snowflake architecture, data modeling, and data pipeline management.
• Technical Skills:
  • Proficiency in SQL for writing and optimizing complex queries in Snowflake.
  • Strong experience with Oracle GoldenGate for Big Data for real-time data replication.
  • Knowledge of Snowpipe for automating data ingestion into Snowflake.
  • Familiarity with Snowflake Data Vault methodology for designing data models.
  • Experience with AWS cloud services (S3, EC2, Lambda, IAM) and integrating Snowflake with AWS tools.
  • Knowledge of CI/CD tools (e.g., Jenkins, GitLab) for automating data engineering workflows.
  • Experience with Databricks for data processing and analytics is a plus.
• Problem-Solving & Communication:
  • Excellent problem-solving skills with a keen attention to detail.
  • Strong communication skills to collaborate effectively with both technical and non-technical stakeholders.
• Collaboration & Documentation:
  • Ability to work in a team environment and share knowledge with peers.
  • Proven track record in maintaining clear and comprehensive documentation for data architecture, pipelines, and processes.

Career Level - IC2

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Disclaimer:

Oracle is an Equal Employment Opportunity Employer*. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans' status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

* Which includes being a United States Affirmative Action Employer

• Snowflake Data Modeling & Architecture:
  Design, develop, and implement scalable and high-performing Snowflake data models and solutions tailored to meet business and data analytics needs. Implement best practices for Snowflake Data Vault methodology and other Snowflake-specific features.
• Real-Time Data Replication & Ingestion:
  Manage and optimize data replication using Oracle GoldenGate for Big Data to facilitate real-time data streaming and transformation into Snowflake. Leverage Snowpipe for efficient and automated data ingestion from various sources into Snowflake.
• Cloud Integration & Management:
  Work with AWS cloud services (e.g., S3, EC2, Lambda) to manage and deploy Snowflake-based data solutions, ensuring smooth integration of cloud resources with Snowflake.
• Data Sharing & Security:
  Utilize SnowShare to securely share Snowflake data across teams and external partners. Implement robust data security measures, including role-based access control (RBAC), data masking, and encryption, to ensure compliance with industry standards.
• CI/CD Implementation:
  Implement and manage CI/CD pipelines for Snowflake development and deployment processes, streamlining the delivery of new data models, transformations, and features.
• Collaboration & Troubleshooting:
  Collaborate with cross-functional teams (data analysts, data scientists, business intelligence teams) to understand data requirements and troubleshoot any data-related issues within Snowflake. Provide support in the resolution of data pipeline challenges.
• Documentation & Best Practices:
  Develop and maintain comprehensive documentation for data architecture, data flows, ETL processes, and Snowflake configurations to ensure knowledge sharing and alignment across the team. Stay updated on emerging trends and best practices in data engineering and Snowflake technologies.
• Performance Optimization:
  Continuously optimize the performance of data pipelines, queries, and overall Snowflake architecture for better efficiency and reduced costs.