The Fountain Group is a national staffing firm and we are currently seeking a ­­­­­­­­Data Intelligence Engineer - Computational Data Pipelines & Integration for a prominent client of ours.  This position is 100% REMOTE. Details for the position are as follows:

Data Intelligence Engineer – Computational Data Pipelines & Integration

Location: Remote
Contract Duration: 4 months (September–December)
Pay Rate: $75–$80/hr
Experience Level: 5–7 years

Position Overview

We are seeking an experienced Data Intelligence Engineer to support a Computational Drug Discovery team focused on improving scientific decision-making through better data capture, storage, and integration.

This individual will design and implement data structures, databases, and automated pipelines used to capture and organize results generated by machine learning models and other scientific computational tools. The goal is to make computational results more readily available to scientists, reducing the time between compound design, data generation, visualization, and analysis.

The ideal candidate will have strong hands-on experience with Python, SQL, relational database design, data pipelines, Docker/containerization, and cloud environments such as AWS EC2, along with exposure to scientific or machine-learning data.

Key Responsibilities

• Design and create relational database tables and schemas to support model inventories, model metadata, metrics, and scientific results.
• Develop and maintain data ingestion and integration pipelines for computational and machine-learning generated data.
• Create and utilize staging tables to effectively manage new data inserts, updates, and integration into existing datasets.
• Capture and organize results generated from custom machine-learning models, scientific calculations, affinity predictions, and other computational workflows.
• Support the containerization of models using Docker and deployment into self-service/cloud-based environments.
• Work with API-based model deployment and integration workflows.
• Partner closely with Computational Drug Discovery scientists to understand data requirements and translate scientific needs into scalable technical solutions.
• Develop Python scripts and automation to streamline data processing and integration.
• Evaluate open-source models, tools, and methods for potential scientific applications.
• Support scientific data standardization, model inventory management, and ontology development.
• As time permits, support virtual molecule enumeration and prediction workflows using approaches such as Free-Wilson analysis and Matched Molecular Pair (MMP) transformations.

Required Qualifications

• 5–7 years of experience in data engineering, scientific data engineering, software engineering, or a related technical field.
• Strong hands-on programming and scripting experience with Python.
• Strong proficiency with SQL.
• Hands-on experience designing relational databases, schemas, and database tables.
• Experience developing ETL/data ingestion, integration, and data pipelines.
• Experience creating and maintaining staging tables and managing database inserts and updates.
• Hands-on experience with Docker or similar containerization technologies.
• Familiarity with AWS EC2 or comparable cloud computing environments.
• Experience working with machine-learning models, model-generated data, scientific data, or prediction results.
• Strong problem-solving, communication, and cross-functional collaboration skills.
• Ability to work independently and deliver high-quality technical solutions within a fast-paced project environment.

Preferred Qualifications

• Previous experience within pharmaceutical, biotechnology, drug discovery, computational chemistry, cheminformatics, or scientific research environments.
• Experience supporting Computational Drug Discovery (CDD) workflows.
• Familiarity with model metadata, model metrics, model inventories, and prediction/result tracking.
• Knowledge of cheminformatics or computational chemistry concepts.
• Experience with FEP+ or related physics-based molecular modeling/scoring methods.
• Familiarity with Matched Molecular Pairs (MMP), Free-Wilson analysis, or virtual molecule enumeration.
• Experience with scientific ontology design or scientific data standardization.
• Experience deploying models or applications through APIs and cloud/self-service platforms.