Data Ops Engineer (Cheminformatics)

Arcadia Inc.; Snowflake (AZ); Remote; USD 110,000 - 170,000; Full time

Benefits offered by this job

Possibility to work remotely

Professional growth opportunities

Company laptop

Job summary

Arcadia Inc. seeks a DataOps Engineer to build, maintain, and optimize data pipelines and infrastructure to support drug design and discovery.

The role evolves data models and ensures scientific data is accessible, reliable, and ready for computational research and AI-driven applications. You will design and maintain data models, develop pipelines in Python, and manage data infrastructure across PostgreSQL and Snowflake, improving data quality and enabling scalable analytics in collaboration

Qualifications

• Hands-on data modeling, database design, architecture, and maintenance.
• Strong experience with PostgreSQL, including database structure, SQL queries, and performance optimization.
• Practical experience with Snowflake and modern data warehousing concepts.
• Proficiency in Python for data processing, pipeline development, and automation.
• Understanding of data engineering principles, including data quality, reliability, scalability, and maintainability.
• Ability to troubleshoot data-related issues and collaborate effectively with software engineers, data scientists, and scientific researchers.

Responsibilities

• Design and maintain data models and databases, ensuring reliability and adaptability.
• Build and optimize data pipelines using Python and modern data engineering practices.
• Manage and evolve data infrastructure across PostgreSQL and Snowflake, extending data models for new sources.
• Improve data quality, accessibility, and scalability through validation, monitoring, and collaboration with teams.

Skills

Data modeling

Database design

Architecture

Data quality

Collaboration

Troubleshooting

Tools

PostgreSQL

Snowflake

Python

API design

Docker

Kubernetes

GitOps

AWS

Job description

We have 30 years of expertise in designing and building custom software systems. We provide software development services focusing on complex high-load applications, AI and BI solutions, and mobile apps.

Efficient management and processing of scientific data can significantly accelerate the discovery and development of new medicines. This is where artificial intelligence, machine learning, and traditional data engineering technologies come together to transform how drug discovery is approached.

We are looking for a DataOps Engineer who will be responsible for building, maintaining, and optimizing data pipelines and infrastructure that support drug design and discovery. The role will also involve maintaining and evolving existing data models, extending them to accommodate additional data modalities, and ensuring that scientific data is accessible, reliable, well-structured, and ready to support computational research and AI-driven applications.

Core Responsibilities

Design and maintain data models and databases, ensuring data structures are reliable, efficient, and adaptable to evolving scientific and business requirements.

Build and optimize data pipelines using Python and modern data engineering practices to ingest, transform, validate, and organize scientific data.

Manage and evolve data infrastructure across PostgreSQL and Snowflake, extending existing data models to support new data sources and modalities.

Improve data quality, accessibility, and scalability by implementing validation and monitoring practices, optimizing data operations, and collaborating with software engineering and scientific teams to support research and AI-driven applications.

Required Experience

Hands-on experience in data modeling, database design, architecture, and maintenance.

Strong experience with PostgreSQL, including database structure, SQL queries, and performance optimization.

Practical experience with Snowflake and modern data warehousing concepts.

Proficiency in Python for data processing, pipeline development, and automation.

Understanding of data engineering principles, including data quality, reliability, scalability, and maintainability.

Ability to troubleshoot data-related issues and collaborate effectively with software engineers, data scientists, and scientific researchers.

Nice to Have

Experience with API design and integration.

Familiarity with AWS or other cloud platforms.

Experience with Docker and containerized applications.

Knowledge of Kubernetes and container orchestration.

Familiarity with GitOps and infrastructure automation practices.

Experience developing or integrating AI-powered applications.

An academic or professional background in life sciences, chemistry, computational biology, bioinformatics, or drug discovery.

Our offer as your future employer

• Collaboration via a B2B contract with payments in USD or EUR, depending on your preference, or through a labor contract
• Possibility to work remotely
• Opportunities for professional growth.
• A company laptop to ensure a comfortable and efficient work setup.