Automation Engineer

Tremco Construction Products Group, Inc.

$90K — $110K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, Data Science, or AI/ML
  • Experience with backend languages like Python, TypeScript/Node.js, or C#/.NET
  • Strong SQL skills and understanding of relational database fundamentals
  • Proficiency in building and consuming REST APIs
  • Solid software engineering skills including OOP and debugging
  • Familiarity with Git workflows and documentation

Responsibilities

  • Lead end-to-end project delivery from requirements gathering to deployment
  • Design and develop internal software and data pipelines
  • Translate business needs into functional and technical specifications
  • Select tools and architectures for scalable solutions
  • Drive execution and rapid delivery of improvements
  • Replace manual workflows with automated production software
  • Create durable data foundations for analytics and operations

Benefits

  • Flexible work environment
  • Opportunities for professional growth and development
  • Access to the latest technologies and tools
  • Collaborative team culture
  • Exposure to innovative projects in automation and data handling
Full Job Description
Job Description

GENERAL PURPOSE OF THE JOB: The Automation Engineer leads end-to-end project delivery, including requirements gathering, system architecture, prototyping, production deployment, and ongoing iteration.

ESSENTIAL DUTIES AND RESPONSIBILITIES:

  • Design and build company-owned internal software, data pipelines, and integrations to replace Excel as an operational backbone


  • Translate ambiguous business needs into clear functional and technical specifications


  • Select appropriate tools, technologies, and architectures to deliver scalable and maintainable solutions


  • Develop robust, efficient, and reliable systems, ensuring accuracy and correctness through validation and testing


  • Drive execution and delivery, shipping improvements quickly and iterating based on feedback and results


  • Replace manual workflows with production software

• Translate informal processes into clear data models, services, and user-facing tools.
• Build internal web apps, APIs, and automation services that users adopt.

  • Build data pipelines and system integrations

• Ingest data from files, forms, SaaS tools, and internal databases.
• Implement ETL/ELT pipelines with validation, lineage, and monitoring.
• Integrate systems via REST APIs, webhooks, queues, and scheduled jobs.

  • Create durable data foundations

• Design relational schemas, enforce constraints, manage migrations.
• Build "single source of truth" datasets for analytics + operations.

  • Operationalize ML

• Use pre-trained models (LLMs / vision / classical ML) for classification, extraction,

routing, forecasting, etc.
• When needed: fine-tune/train on company data, evaluate properly, deploy with

monitoring.

EDUCATION: Computer Science, Computer Engineering, Data Science, AI/ML

EXPERIENCE:

  • Proficiency in at least one backend language: e.g. Python, TypeScript/Node.js, or C#/.NET


  • Ability to build automation/services that handle data, integrate APIs, and run reliably in production.


  • Strong SQL and relational fundamentals (schema design basics, joins/aggregations, constraints, query debugging; Postgres/MySQL/SQL Server).


  • Ability to build and consume REST APIs (HTTP basics, auth patterns, pagination, error handling).


  • Solid software engineering fundamentals:

• data structures + OOP
• readable, maintainable code
• debugging and refactoring

  • Git workflow experience (branches, pull requests, code review habits).


  • Clear technical communication: can write clean documentation


OTHER SKILLS AND ABILITIES:

  • Framework experience: FastAPI / Flask / Django, ASP.NET Core, NestJS / Express


  • Data pipeline orchestration tooling: Airflow / Dagster / Prefect


  • Docker experience and basic CI/CD (GitHub Actions, Azure DevOps, or similar)


  • Cloud exposure (AWS / Azure / GCP): managed databases, object storage, message queues


  • Building SaaS integrations using REST APIs / webhooks

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