Automation Engineer
Indexed description
- D esign 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
○ Build internal web apps, APIs, and automation services that users adopt.
- Build data pipelines and system integrations
○ Implement ETL/ELT pipelines with validation, lineage, and monitoring.
○ Integrate systems via REST APIs, webhooks, queues, and scheduled jobs.
- Create durable data foundations
○ Build “single source of truth” datasets for analytics + operations.
- Operationalize ML
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:
○ readable, maintainable code
○ debugging and refactoring
- Git workflow experience (branches, pull requests, code review habits).
- Clear technical communication: can write clean documentation
- 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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