Modinity is a retail lifestyle company that curates several brands, including Buttonscarves, Buttonscarves Beauty, Benang Jarum, Nada Puspita, Zyta Delia, Calla The Label, and TYGA. Currently, we have over 100 stores throughout Indonesia and the principal of our Malaysian entity of Buttonscarves with a current count of 6 stores. We just expanded our store into Singapore market and looking forward to do enlarge our expansion globally.
Currently, we are looking for a strategic Head of QA & Data Integrity to lead our Quality Engineering division. This is not a traditional testing role; it is a technical leadership position. Reporting to the CTO, you will be responsible for the Quality of Engineering across our entire stack. You will possess a unique blend of Software Architecture knowledge (React/Node) and Data Engineering standards (Python/dbt).
Responsibilities :
1. Shopify Ecosystem QA (The Frontend & Code Standards)
- Code Quality Governance: Review architectural decisions and Pull Requests for Shopify Themes and Headless implementations. Ensure code is modular, performant, and testable.
- Automated Validation: Oversee the creation of robust test automation frameworks (Cypress/Playwright) that validate UI/UX consistency across devices.
- Transaction Logic: Rigorously validate complex cart scenarios to ensure calculated totals are mathematically correct at the code level before checkout.
2. Data QA & BI Validation (ETL and BI Tools)
- ETL Pipeline Auditing (Airflow/Python): Review and audit the Python scripts used in our Airflow DAGs. Ensure exception handling is robust and that data flows from ERP/Shopify to our Warehouse without duplication or silent failures.
- Data Modeling Validation (dbt): act as the Technical Reviewer for data transformations. Verify that dbt models and SQL logic accurately represent business rules (e.g., verifying how Net Revenue is calculated in the code).
- BI Dashboard Integrity: Systematically test output shown in BI Tools against source data to ensure alignment with business definitions.
3. Integration & API Resilience
- End-to-End Sync: Test the flow of data between distributed systems. Validate payload structures and API contracts between Shopify, ERP, and the Data Warehouse.
- Error Handling & Alerts: Simulate API failures to ensure the system handles data gaps gracefully. Ensure that alerts (PagerDuty/Slack) are triggered immediately when data syncs fail.
4. Engineering Standards & CI/CD Strategy
- Release Architecture: Own the Quality Gates in our CI/CD pipelines. Ensure that no code reaches production unless it passes static analysis, unit tests, and integration checks.
- Data Observability: Implement automated observability (e.g., Monte Carlo, custom dbt tests) to alert the team if data becomes stale or inaccurate before the business notices.
- Root Cause Analysis: Lead technical post-mortems. When a bug occurs, trace it back to the specific commit or logic error and implement process changes to prevent recurrence.
5. Leadership & Governance
- Define the Standard: Establish the Definition of Done for both App features and Data projects.
- Technical Mentorship: Manage and mentor a team of SDETs and Data QA Engineers. Review their code, uphold engineering best practices, and help them grow into technical leaders.
- Planning & Estimation: Participate in quarterly planning to estimate the effort required for validation and ensure quality is baked into the roadmap, not an afterthought.
Requirements :
- Minimum 10+ years in Engineering, with a specific focus on Quality Assurance, SDET, or Data Engineering.
- Engineer First Mindset: You are comfortable reading and debugging source code. You view Quality Assurance as an engineering discipline, not just manual verification.
- Ability to communicate clearly on technical topics to both engineers and C-level executives.
Core Tech Stack & Skills:
- Polyglot Fluency: You must have deep professional experience reading and auditing code in Python (for Data/Back-end) AND JavaScript/TypeScript (for Front-end/Node.js).
- Data Engineering Context: Strong proficiency with SQL is mandatory. You must understand Airflow DAGs, dbt data modeling, and Cloud Data Warehouses (BigQuery/GCP).
- App Engineering Context: Experience with Modern Web Stacks (React, Next.js, Node.js) and REST/GraphQL APIs.
- Infrastructure & DevOps: Familiarity with Cloud Platforms (GCP preferred), Docker, and CI/CD workflows (GitHub Actions/GitLab CI).
