Data Science Platform & DevOps Engineer

Ampstek; Juno Beach (FL); Presencial; USD 83.000 - 152.000; Jornada completa

Descripción de la vacante

Ampstek is seeking a Data Science Platform & DevOps Engineer for a contract role in Florida. You will engineer and operationalize the Python-based PSO analytics platform, building CI/CD pipelines, AWS CDK components, and automated tests to support forecasting and energy market analysis.

You will also collaborate on semantic and reporting layer integration, data access patterns, and production support to deliver scalable data platform solutions.

Formación

• Five or more years of experience in software engineering, data engineering, data science platforms, or DevOps delivery.
• Strong Python development experience with data-oriented applications, APIs, packages, testing frameworks, and dependency management.
• Hands-on experience with GitHub, GitHub Actions, CI/CD pipelines, pull-request controls, and release automation.
• Experience with AWS CDK, Terraform, CloudFormation, or comparable infrastructure-as-code tools.
• Experience integrating analytics applications with cloud storage, databases, query services, semantic layers, or reporting tools.
• Experience with automated testing, deployment validation, environment management, monitoring, and production support.
• Strong troubleshooting, technical documentation, communication, and stakeholder collaboration skills.

Responsabilidades

• Develop and support Python-based data science, analytics, forecasting, and optimization applications.
• Translate requirements into maintainable application, data access, and deployment solutions.
• Enhance GitHub repository structure, branching strategy, PR controls, code review standards, and release management.
• Build and maintain CI/CD pipelines (GitHub Actions) for build, test, security checks, packaging, deployment, and rollback.
• Develop and maintain AWS CDK components and reusable deployment patterns with the cloud engineer.
• Support semantic and reporting layer evaluation, metadata configuration, query interfaces, caching, and integration.
• Implement automated unit, integration, regression, deployment, and smoke tests with traceable results.
• Manage application configuration, secrets, environment promotion, observability, and production troubleshooting.
• Create technical designs, test plans, deployment guides, release documentation, and runbooks.

Conocimientos

Python programming

CI/CD automation

GitHub Actions

AWS CDK

Terraform

Data engineering

Herramientas

GitHub

Google Cloud?

AWS CloudFormation

Terraform

Descripción del empleo

Job Title: Data Science Platform & DevOps Engineer

Employment Type: Contract

Project Overview

The Power Systems Optimizer (PSO) platform supports energy market analysis, forecasting, optimization, scenario evaluation, and data-driven operational decision-making.

Position Summary

The Data Science Platform & DevOps Engineer will engineer and operationalize the Python-based PSO analytics platform. The role combines application and data platform engineering with GitHub workflow design, CI/CD automation, AWS CDK, automated testing, environment management, and production support. The engineer will also support semantic and reporting layer integration, data access patterns, and prioritized enhancements needed for forecasting and energy market analysis.

Key Responsibilities

• Develop and support Python-based data science, analytics, forecasting, and optimization applications.
• Translate functional and technical requirements into maintainable application, data access, and deployment solutions.
• Enhance GitHub repository structure, branching strategy, pull-request controls, code review standards, and release management.
• Build and maintain GitHub Actions or comparable CI/CD pipelines for build, test, security checks, packaging, deployment, and rollback.
• Develop and maintain AWS CDK components and reusable infrastructure deployment patterns in coordination with the cloud engineer.
• Support semantic and reporting layer evaluation, metadata configuration, query interfaces, caching, and application integration.
• Implement automated unit, integration, regression, deployment, and smoke tests, including test evidence and defect resolution.
• Manage application configuration, secrets integration, environment promotion, observability, and production troubleshooting.
• Create technical designs, test plans, deployment guides, release documentation, and operational runbooks.

Required Qualifications

• Five or more years of experience in software engineering, data engineering, data science platforms, or DevOps delivery.
• Strong Python development experience with data-oriented applications, APIs, packages, testing frameworks, and dependency management.
• Hands-on experience with GitHub, GitHub Actions, CI/CD pipelines, pull-request controls, and release automation.
• Experience with AWS CDK, Terraform, CloudFormation, or comparable infrastructure-as-code tools.
• Experience integrating analytics applications with cloud storage, databases, query services, semantic layers, or reporting tools.
• Experience with automated testing, deployment validation, environment management, monitoring, and production support.
• Strong troubleshooting, technical documentation, communication, and stakeholder collaboration skills.

Preferred Energy Market Experience

• Experience supporting energy market analytics, forecasting, optimization, trading, utility, or renewable energy platforms.
• Familiarity with wholesale power markets, generation operations, transmission systems, or energy trading processes.
• Experience working with market, operational, forecasting, or asset-performance datasets in the energy industry.
• Knowledge of ISO/RTO market structures, including ERCOT, PJM, MISO, SPP, CAISO, NYISO, or ISO-NE.
• Experience with cloud analytics, reporting, visualization, semantic-layer, machine learning, or optimization solutions.
• Updated GitHub repositories, CI/CD workflows, branching standards, and release controls.
• Reusable CDK or infrastructure-as-code enhancements.
• Python application, data access, semantic-layer, and reporting integration deliverables.
• Automated test suites, test plans, deployment validation, and test results.
• Production deployment documentation, knowledge transfer, and support runbooks