Senior Data Engineer
About Modelyst
Modelyst is a three-person engineering firm building production data and machine learning infrastructure for manufacturing and research environments. Our systems support real-world operations and are designed to be reliable, maintainable, and cost-efficient.
We partner directly with academic labs, national laboratories, and enterprise R&D organizations, and maintain long-term engagements in the industrial sector.
The Role
We are hiring a Senior Data Engineer to take a designed-but-not-yet-deployed AWS data platform from architecture to a working MVP at a customer site in Japan and then own its operation from there.
You will work on implementing the system, getting it running in the customer's AWS environment, and proving it out at MVP scope and beyond. You'll be the person accountable for the platform from "deployed" through "running reliably in production" through "evolving as the workload grows."
Initial work is focused on standing up infrastructure, implementing data pipelines and backend services, validating the system end-to-end against real operational data, and bringing it live for an MVP deployment. After launch, the role shifts to operating and extending the platform as the engagement matures.
This role is best suited for engineers who take pride in shipping into production environments where reliability matters and want to own a system over a multi-year arc rather than handing it off.
You will focus on this single engagement rather than being fragmented across multiple projects.
Responsibilities
Implement the designed data pipelines and backend services in Python on AWS
Design and manage AWS infrastructure using Terraform and Terragrunt
Deploy the platform end-to-end into the customer's AWS environment and bring it live for an MVP launch, validating against real operational data
Build out the CI/CD, observability, and runbooks needed to operate the platform reliably
Own the platform after launch — incident response, performance, capacity, and cost
Lead the platform's design evolution from MVP through later production stages, making the data model, scaling, and reliability decisions informed by running it yourself
Requirements
Senior-level experience shipping production backend or data services in Python. You have built systems other people depend on.
Production AWS architecture experience, including event-driven services such as SQS, SNS, EventBridge, Step Functions, and Lambda.
Infrastructure-as-code with Terraform (Terragrunt is our standard). You've stood up and managed cloud infrastructure end-to-end.
You've operated systems in production — incident response, performance, capacity, cost — and been the person accountable when something breaks.
Preferred Experience
Time-series or high-throughput data environments
Industrial, manufacturing, robotics, or IoT systems
ML infrastructure or MLOps tooling
Large-scale data processing frameworks (e.g., Spark, Databricks)
Japanese language ability is helpful but not required
Location
Remote within the United States
Must be authorized to work in the U.S. without employer sponsorship
Collaboration with Japan-based teams is common; Pacific or Mountain time preferred
Occasional travel to Japan may be available
Apply To This Job
Modelyst is a three-person engineering firm building production data and machine learning infrastructure for manufacturing and research environments. Our systems support real-world operations and are designed to be reliable, maintainable, and cost-efficient.
We partner directly with academic labs, national laboratories, and enterprise R&D organizations, and maintain long-term engagements in the industrial sector.
The Role
We are hiring a Senior Data Engineer to take a designed-but-not-yet-deployed AWS data platform from architecture to a working MVP at a customer site in Japan and then own its operation from there.
You will work on implementing the system, getting it running in the customer's AWS environment, and proving it out at MVP scope and beyond. You'll be the person accountable for the platform from "deployed" through "running reliably in production" through "evolving as the workload grows."
Initial work is focused on standing up infrastructure, implementing data pipelines and backend services, validating the system end-to-end against real operational data, and bringing it live for an MVP deployment. After launch, the role shifts to operating and extending the platform as the engagement matures.
This role is best suited for engineers who take pride in shipping into production environments where reliability matters and want to own a system over a multi-year arc rather than handing it off.
You will focus on this single engagement rather than being fragmented across multiple projects.
Responsibilities
Implement the designed data pipelines and backend services in Python on AWS
Design and manage AWS infrastructure using Terraform and Terragrunt
Deploy the platform end-to-end into the customer's AWS environment and bring it live for an MVP launch, validating against real operational data
Build out the CI/CD, observability, and runbooks needed to operate the platform reliably
Own the platform after launch — incident response, performance, capacity, and cost
Lead the platform's design evolution from MVP through later production stages, making the data model, scaling, and reliability decisions informed by running it yourself
Requirements
Senior-level experience shipping production backend or data services in Python. You have built systems other people depend on.
Production AWS architecture experience, including event-driven services such as SQS, SNS, EventBridge, Step Functions, and Lambda.
Infrastructure-as-code with Terraform (Terragrunt is our standard). You've stood up and managed cloud infrastructure end-to-end.
You've operated systems in production — incident response, performance, capacity, cost — and been the person accountable when something breaks.
Preferred Experience
Time-series or high-throughput data environments
Industrial, manufacturing, robotics, or IoT systems
ML infrastructure or MLOps tooling
Large-scale data processing frameworks (e.g., Spark, Databricks)
Japanese language ability is helpful but not required
Location
Remote within the United States
Must be authorized to work in the U.S. without employer sponsorship
Collaboration with Japan-based teams is common; Pacific or Mountain time preferred
Occasional travel to Japan may be available
Apply To This Job