Staff, Data Engineer (Global Security)

WHAT WILL YOU DO?

Build and Optimize Data Pipelines

Design, develop, and enhance scalable ETL/ELT pipelines to migrate, transform, and load large datasets from diverse sources (e.g., databases, APIs, flat files), ensuring seamless integration for analytics, reporting, and AI solutions.

Drive Technical Innovation

Leverage advanced tools and techniques to create reusable, secure, and efficient technical solutions that align with business needs and project lifecycle deliverables, including data sharing and governance.

Champion Snowflake Best Practices

Guide users on effective Snowflake utilization, establishing standards for data consumption, storage, and workflow integration while designing and implementing high-impact stored procedures.

Collaborate Across Teams

Partner with cross‑functional stakeholders to translate data requirements into robust solutions that empower analytics, reporting, AI, and machine learning initiatives.

Strengthen Data Governance

Implement and maintain best practices for metadata management, access controls, and compliance to ensure data integrity and security.

Ensure Performance and Scalability

Monitor system performance, troubleshoot issues, and optimize queries/processes to maximize efficiency and scalability.

Automate and Streamline Workflows

Use Python scripting and orchestration tools (e.g., Airflow) to automate platform utilities and workflows, reducing manual effort and enhancing reliability.

Document and Share Knowledge

Create clear technical documentation for processes, architectures, and data models to foster team collaboration and institutional knowledge.

What Do You Need to Succeed?

Snowflake Expertise

• Hands‑on experience with Snowflake (designing stored procedures, optimizing queries, data storage/consumption best practices).

Data Pipeline Development

• Proficiency in building, optimizing, and maintaining ETL/ELT pipelines for large‑scale data migration and transformation.

Programming & Automation

• Strong scripting skills in Python for automation and tool development; experience with workflow orchestration tools (e.g., Airflow).

Data Governance & Security

• Knowledge of implementing data governance practices (metadata management, access controls, compliance).

Performance Optimization

• Skills in monitoring, troubleshooting, and optimizing database/query performance for scalability.

Nice‑to‑Have

• Technical Collaboration & Communication: Ability to guide users/teams on platform best practices and present technical solutions in cross‑functional meetings.
• DevOps & CI/CD Practices: Knowledge of version control (Git), containerization (Docker), or CI/CD pipelines for data engineering workflows.
• Documentation & Knowledge Sharing: Experience documenting technical processes, architectures, and data models for team use.
• Advanced Data Modeling: Experience designing dimensional models (e.g., star/snowflake schemas) or optimizing data warehouses for analytics.