Data and Quality Operations Analyst – Facade Engineering

About Arcanys

Arcanys launched in 2010 to help entrepreneurs and innovators from Australia, Europe and other parts of the globe accelerate their software development with dedicated remote engineers from the Philippines.

Why work with us?

There’s a unique spirit to Arcanys—manifested in our amazing talents, passion for technology, and a strong focus on everyone’s career and personal development. As a company rooted in people, we make a point to offer exciting work opportunities and foster a culture that everyone can meaningfully contribute to.

Join us as a: Data and Quality Operations Analyst – Facade Engineering

We are looking for a Data & Quality Operations Analyst to own the quality of our facade inspection projects end-to-end. You will review image data, annotations, and AI outputs, and analyze facade engineering productivity and quality at both individual and team levels. You will also drive consistency, documentation, and processes for how our engineering team works.

This role sits at the intersection of data analysis, quality assurance, and operations. Your job is to turn messy reality (projects, people, data) into clear metrics, insights, and standard ways of working.

In this role you’ll get to:

1. Project & Data Quality

• Review image quality, annotation quality, and AI output quality across projects.

• Define and maintain quality criteria (e.g., acceptable image sharpness, annotation standards, acceptable AI error ranges).

• Build and maintain quality dashboards and reports for ongoing projects.

• Identify recurring quality issues and feed them back into training, tooling, and process improvements.

2. Productivity & Performance Analytics

• Measure individual and team-level productivity for facade engineers (e.g. tasks completed, throughput, rework rate, cycle time).

• Develop and track quality KPIs per person/team (e.g. defect rate, correction rate, review turnaround time).

• Run root-cause analyses when performance or quality drops (data, process, skill, workload, unclear specs, etc.).

• Provide regular performance reports to engineering leadership to support coaching, hiring, and workload decisions.

3. Process, Documentation & Consistency

• Map how we currently work (intake → data → annotation → AI → engineering output → client deliverables).

• Identify inconsistencies and gaps in process, handoffs, and documentation.

• Create and maintain clear SOPs, checklists, and documentation for how the engineering team should work, including QA steps.

• Ensure all processes are version-controlled, discoverable, and actually used (not just sitting in a Notion graveyard).

• Work with team leads to align on standard ways of working across projects and clients.

4. Tooling & Automation Support

• Work with product/engineering to define data and reporting requirements for internal tools and dashboards.

• Help design and validate quality-check automations (e.g., image checks, annotation consistency checks, AI sanity checks).

• Continuously refine metrics, definitions, and thresholds as the product and workflows evolve.

5. Cross-Functional Collaboration

• Collaborate closely with facade engineers, AI/ML team, project managers, and ops.

• Present insights and recommendations in a way that is actionable, not academic.

• Push for changes when data clearly shows a problem, even if it is uncomfortable.

What you’ll need to succeed:

• 3+ years in a data analyst / operations analyst / quality analyst role.

• Experience building dashboards and reports (e.g. Looker, Power BI, Tableau, Metabase, or similar).

• Proven experience working with operational / production data, not just marketing or finance reporting.

• Strong analytical and critical thinking skills; able to challenge assumptions and spot weak metrics.

• Obsession with detail and quality – comfortable reviewing sample data (images, annotations, AI outputs)