Data Engineer

About the job
Steradium is a technology company focused on turning advanced artificial intelligence into practical solutions for businesses. Our team members collaborate in a modern, fast-paced environment that values curiosity, problem-solving, and continuous learning. We're currently building an AI-native engineering team from the ground up.
What you'll do
The Data Engineer will design, build, and maintain scalable data pipelines that support analytics, AI models, and business intelligence initiatives.
Daily responsibilities include developing and optimizing ETL processes, implementing data models, and managing data warehouses to ensure reliable, high-quality data.
The role involves collaborating with stakeholders to understand data needs, define technical requirements, and deliver robust solutions.
The Data Engineer will also monitor data workflows, troubleshoot issues, improve performance, and contribute to best practices in data engineering and governance.
What we're looking for
Strong data engineering skills, including experience with building and maintaining production data pipelines.
Proficiency in data modeling and designing structured and scalable data architectures.
Hands-on experience with Extract Load Transform (ELT) processes and tools for ingesting and transforming data.
Knowledge of data warehousing concepts and platforms used for storing and organizing analytical data.
Ability to work with data analytics teams to deliver datasets that enable reporting, dashboards, and AI/ML use cases.
Proficiency in one or more programming languages commonly used in data engineering (e.g., Python, Java, or Scala).
Experience with cloud data platforms and services (e.g., AWS, Azure, or GCP) is highly beneficial.
Familiarity with SQL and relational databases; exposure to NoSQL or big data technologies is a plus.
Strong problem-solving skills, attention to detail, and ability to work independently in a remote environment.
How we work with AI
Everyone has access to Google Gemini, Claude Code, and Codex in their daily loop. We measure output in shipped, reviewed, production-grade work, not in hand-typed lines.
Everyone can use AI assistants for scaffolding, refactors, test generation, and exploring unfamiliar codebases, but people own every line that ships with their name on it.
We expect everyone to know and understand where AI is unreliable: subtle concurrency, security boundaries, domain logic, anything where being confidently wrong is expensive.
What we offer
Competitive salary package and 13th month pay
Government-mandated benefits (SSS, PhilHealth, Pag-IBIG)
Flexible working arrangements
Annual learning budget plus company-provided AI tooling
Mentorship program
No graveyard shift, no mandatory overtime

Work arrangement
Yes