ABOUT THE COMPANY

Growth Protocol is an Enterprise Reasoning Platform headquartered in New York. We help enterprises turn strategy into action by combining a neuro-symbolic reasoning core, seamless data activation, and agentic workflows. Our platform reduces execution drag, accelerates growth, and delivers measurable business outcomes for some of the world’s most complex organizations.

JOB DESCRIPTION

We are searching for an ambitious go-getter who welcomes the challenge of meeting the needs of a hyper-growth startup. As a Sr. Data Engineer, you will be at the heart of Growth Protocol’s data infrastructure, playing a foundational role in building the systems that power our AI platform. Your work will directly influence product features, client outcomes, and strategic business decisions.

You will collaborate with Data Scientists, Backend Engineers, Client IT, and business stakeholders to build and maintain scalable pipelines that serve billions of rows of structured and unstructured data weekly, enabling high-impact insights across multiple industries.

THE ROLE

Growth Protocol is hiring a Senior Data Engineer to play a foundational role in building the systems that power our AI platform. You will be at the heart of Growth Protocol's data infrastructure, directly influencing product features, client outcomes, and strategic business decisions.

Ideal candidates are ambitious go-getters who welcome the challenge of meeting the needs of a hyper-growth startup and bring deep technical expertise across modern data infrastructure and ML operationalization.

OBJECTIVES OF THE ROLE

Collaboration

Work closely with Data Scientists to translate business and ML requirements into robust data workflows
Ensure timely delivery of clean, reliable data to support model development and production features

Technical Development

Engineer and manage scalable ETL architecture using Airflow, Snowpark, Cloud Run, and Apache Beam
Design and implement a high-performance data infrastructure for seamless processing and integration
Extract data from diverse online platforms
Operationalize machine learning models, focusing on deployment, reliability, and performance

Data Connectivity

Partner with client IT teams to identify the most efficient and secure methods for data ingestion including Snowflake Sharing, Databricks Delta Sharing, Private Link, and VPN
Work alongside the Platform Engineering team to define requirements for secure networking paths that support high-performance data transfers
Perform end-to-end testing of client connections to ensure data integrity and connectivity
Integrate customer databases with our platform

Monitoring and Reliability

Create and manage real-time monitoring systems for data ingestion and transformation pipelines
Proactively identify and resolve issues to maintain high levels of system reliability and data integrity

REQUIRED SKILLS AND QUALIFICATIONS

5+ years of experience in Data Engineering
Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
Experience building data pipelines with robust unit and integration testing
Proficiency in distributed computing frameworks including Apache Beam and Spark
Functional understanding of enterprise networking including VPC peering, Private Link, and VPNs, with the ability to troubleshoot connectivity in a cloud environment
Hands-on experience operationalizing ML models in production
Familiarity with ML/AI, NLP, and Data Science workflows including MLFlow
Deep understanding of ETL workflows, data modeling, and data architecture
Strong debugging and problem-solving skills
Excellent communication skills and experience collaborating across teams

Preferred Qualifications

Experience working on enterprise products serving Fortune 500 clients across Financial Services, Industrials, and Consumer Products
Prior startup experience
Interest in current events, market dynamics, and emerging technologies
Experience creating Agent Skills
Familiarity with APIs and web scraping for data collection
Familiarity with Graph Databases