Neon One is looking to architect and manage robust data pipelines and infrastructure that support ML/AI workflows, analytics, and business intelligence across their platform. The goal is to empower product, marketing, and finance teams with clean, centralized, and actionable data to drive insights and innovation.
Requirements
- 4+ years of hands-on experience in a data engineering role, with a proven track record of building and maintaining data infrastructure in a production environment.
- Deep expertise in designing, building, and optimizing data pipelines and ETL/ELT workflows.
- Advanced proficiency in SQL for complex data manipulation and extraction.
- Hands-on experience with a cloud data warehouse, preferably Snowflake.
- Experience with streaming data technologies (e.g., Snowpipe Streaming, Fivetran, Kafka).
- Direct experience implementing and working with a medallion architecture, preferably Snowflake.
- Demonstrated experience working within a major cloud ecosystem, deploying infrastructure and tools, ideally AWS.
Responsibilities
- Architect & Build Data Pipelines: Design, build, and maintain robust and scalable data pipelines to ingest large data sets from customer-facing products, internal systems, and third-party sources into our cloud data platform.
- Create a Centralized Data Hub: Be a key player in creating a centralized data hub that empowers not only our AI and ML initiatives but also provides clean, reliable data for analytics, reporting, and strategic insights across the entire business.
- Enable AI & ML Operations: Create and manage the operational data pipelines essential for the training, validation, and deployment of machine learning models in collaboration with the Data Scientist and AI Engineer.
- Implement Data Modeling & Governance: Develop and manage our data warehouse using a medallion architecture (Bronze-Silver-Gold) in Snowflake. Ensure data is progressively cleansed, structured, and optimized for a wide range of applications, from business intelligence to advanced AI.
- Develop and Manage Data Transformations: Use tools like DBT to build, test, and deploy data transformation logic, ensuring the highest standards of data quality, integrity, and reliability.
- Collaborate and Empower: Partner closely with stakeholders across the organization to understand their data needs and provide them with the data and tools they need to be successful.
- Optimize and Scale: Own the performance, scalability, and cost-effectiveness of our data infrastructure. You will be responsible for monitoring, maintaining, and optimizing our data stack.
Other
- This is a role for someone who thrives in ambiguity, enjoys end-to-end system ownership, and is passionate about designing scalable solutions from the ground up.
- A Bachelor's degree in a quantitative field such as Computer Science, Engineering, Statistics, or a related discipline.
- Proficiency in Python and data engineering libraries.
- Familiarity with modern data transformation tools, particularly dbt.
- Familiarity with MLOps principles and tools.
