Staff Data Engineer

About the Role

The STAFF DATA ENGINEER role focuses on designing and maintaining scalable, secure, and efficient data pipelines and services that support analytics and product insights. This position is critical for evolving the data platform's architecture and ensuring reliable, cost-effective data operations. The engineer will contribute to data governance, mentor team members, and lead cross-functional initiatives that enhance operational excellence and data infrastructure.

About Our Client

The organization operates in the health and fitness technology sector, providing tools and resources to support users in achieving their health goals through data-driven insights. It maintains a broad ecosystem that includes mobile and web applications, a large nutrition database, and data science and AI/ML assets. The company addresses challenges in health management by leveraging advanced data engineering and analytics to empower users in their fitness journeys.

Responsibilities

• Design, build, and maintain high-throughput, event-driven data services and orchestration pipelines.
• Architect and evolve core platform components to modernize data ingestion and pipelines.
• Collaborate on DataOps best practices including data modeling, CI/CD, testing, and validation.
• Define and promote data engineering standards across the organization.
• Advance agentic development maturity and safe AI usage.
• Shape data governance strategies for secure and consistent data access.
• Manage cost efficiency and stewardship of data infrastructure.
• Mentor engineers to foster technical growth and alignment on architecture.
• Lead cross-functional projects spanning infrastructure and observability.
• Collaborate with product and engineering teams to resolve complex data challenges.
• Develop and maintain secure, compliant, and well-governed data systems.

Requirements

• 8 to 12+ years in engineering roles with 3 to 5+ years in senior-level data engineering.
• Extensive experience with Snowflake or similar data stores.
• Proficiency in multiple data storage technologies (RDS, MySQL, MongoDB, DynamoDB, Redis, S3).
• Advanced SQL skills for large-scale datasets.
• Experience with Airflow or similar orchestration tools.
• Skilled in Python for high-throughput services.
• Knowledge of event-driven architectures including CDC and Kafka.
• Familiarity with various API design patterns.
• Strong DataOps skills including CI/CD pipelines and testing frameworks.
• Experience implementing monitoring and alerting for observability.
• Knowledge of infrastructure-as-code tools like Terraform.
• Experience with AI-assisted development workflows and engineering standards.