Senior Data Engineer

Growth Protocol

About The Position

Growth Protocol is 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.

Requirements

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

Nice To Haves

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

Responsibilities

Collaborate 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.
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.
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.
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.