Salary Range:

100,000 - 180,000

About Us:

Absinthe Labs is an AI company that uses data-driven reinforcement learning and a suite of advanced sybil detection mechanisms to give you highly adaptive and accurate profiling of your users and user behavior. We provide Points-as-a-Service powered by AI with built-in sybil detection.

Absinthe is funded by 6th Man Ventures, Geometry Ventures, Builder Capital, Tangent Ventures, Room40 Ventures and Outlier Ventures.

About The Role:

Absinthe Labs is currently looking for our first in-house data engineer. Specifically, a data engineer with a specialization in building and managing data pipelines. Someone who is responsible for the architecture, construction, and maintenance of scalable data infrastructure. This individual's core responsibilities include:

Key Responsibilities:

  1. Data Ingestion: Developing efficient pipelines for importing raw data from various sources into a centralized data storage solution. This process involves selecting the right tools and technologies to ensure timely and accurate data flow.
  2. Data Cleaning and Preparation: Implementing pipelines designed to clean, validate, and preprocess data. This step is crucial for ensuring the quality and reliability of data, removing inaccuracies, and transforming data into a format suitable for analysis.
  3. Data Access Pipelines: Designing and optimizing queries, using GraphQL or SQL, to facilitate efficient and secure access to data. This involves understanding the data needs of data scientists and analysts, and crafting queries that provide them with the exact data required for their work, whether for model training or inference tasks.
  4. Data Table and Model DevOps: Managing the lifecycle of data tables and machine learning models through development operations (DevOps) practices. This includes version control, testing, deployment, and monitoring of data structures and models to ensure they are up-to-date, accurate, and performing optimally in production environments.
  5. Experience in blockchain data: extracting data from various blockchain networks, and integrating this data into the existing data infrastructure is preferred. The ability to work with blockchain data APIs for ingestion and processing of transaction data, smart contract interactions, and other on-chain activities adds a valuable dimension to the role.

The ideal candidate for this role should possess a strong computer science foundation, extensive database management experience, proficiency in SQL and GraphQL for querying purposes, and a deep understanding of data pipeline tools and data modeling practices. Additionally, knowledge in DevOps tools and methodologies for both data and model management is essential to ensure seamless integration and continuous improvement of data processes and machine learning models in a dynamic data environment.

Requirements: