Staff Level Data Engineer

*This is an in-office position (5 days per week) located in NYC's Flatiron neighborhood and offered on a full time permanent basis.*
A Next Ventures client is seeking a Staff Level Data Engineer with infrastructure experience.
As a leader on the Data & Infrastructure team, you will design, build, and maintain the scalable infrastructure that supports AI research and products. In addition to architecting systems to handle large-scale data and computation, you will play a critical role in shaping our engineering culture and growing the early engineering team.
This role offers the opportunity to collaborate closely with co-founders and the head of engineering to define the vision for company infrastructure and ensure systems meet both internal and customer needs.
Key Responsibilities:
Define and own the vision for data and infrastructure, ensuring alignment with business and product goals.
Architect and implement scalable server infrastructure to meet customer demands and internal requirements.
Build and optimize ETL pipelines for efficient data movement, transformation, and storage.
Design and configure systems for optimal scaling, performance, and fault tolerance.
Develop alerts and monitoring systems to ensure reliability and proactively identify issues.
Collaborate with cross-functional teams, including product managers, researchers, and co-founders, to align infrastructure priorities.
Foster a strong engineering culture and mentor early team members as the team scales.
Requirements:
5-10 years of designing and building data and/or core infrastructure; open to exceptional candidates with fewer years and evidence of exceptional ability
Infrastructure Design and Scaling :
Proven experience designing scalable, fault-tolerant systems for large-scale data processing and real-time applications.
Expertise in server architecture and configuration for high availability and scalability.
Data Engineering :
Proficiency in building ETL pipelines using modern tools like Apache Airflow, dbt, or similar frameworks.
Experience with distributed data systems (e.g., Kafka, Spark, Hadoop) for batch and real-time processing.
Cloud Platforms :
Deep knowledge of cloud services (e.g., AWS, GCP, or Azure), including compute, storage, and networking components.
Hands-on experience with containerization and orchestration (e.g., Docker, Kubernetes).
Monitoring and Reliability :
Familiarity with observability tools such as Prometheus, Grafana, and the ELK stack for performance monitoring.
Experience setting up automated alerts and self-healing mechanisms for production systems.
Programming and Automation :
Strong programming skills in Python, Go, or a similar language, with a focus on building maintainable, production-grade systems.
Expertise in Infrastructure-as-Code tools (e.g., Terraform, CloudFormation) for managing cloud infrastructure.
Preferred Qualifications:
Experience working in fast-paced startup environments, ideally with early-stage teams.
Familiarity with AI/ML infrastructure requirements, such as distributed training or model serving pipelines.
A track record of mentoring engineers and helping shape technical culture within a growing team.