Odoo
Interview Questions

Tests understanding of both Accounting and Inventory — look for nuanced comparison based on use cases, not just features.

Assess real-world Accounting experience — depth of knowledge, problem-solving, and results achieved.

Look for scalability thinking — performance considerations, user management, and Inventory-specific best practices.

Tests practical Manufacturing knowledge — implementation steps, dependencies, and troubleshooting experience.

Reveals the candidate's specialization, passion, and ability to articulate the strategic value of their expertise.

Look for phased scaling approach — horizontal scaling, caching layers, database optimization, and Odoo/Accounting-specific patterns.

Should mention OWASP top 10 risks relevant to Odoo and Accounting, authentication, authorization, and input validation.

Tests data architecture skills — should consider query patterns, consistency requirements, and how Accounting interacts with the data layer.

Look for Odoo/Accounting-specific code review criteria beyond generic best practices — framework conventions, performance gotchas, and security patterns.

Tests learning agility — look for structured learning approach, resource utilization, and ability to deliver while learning.

Look for professional communication — evidence-based advocacy, willingness to compromise, and focus on outcomes over ego.

Assess continuous learning habits — official documentation, community involvement, conferences, certifications, and personal projects.

Tests leadership potential — structured knowledge sharing, patience, and ability to adjust communication to skill level.

Look for understanding of Odoo's schema, relationship types, and practical experience customizing the data model.

Should cover user roles, record types, automation rules, integrations, and data migration strategy for Accounting.

Look for answers about query optimization, bulk operations, governor limits, and caching strategies specific to Odoo.

Should cover role hierarchies, sharing rules, field-level security for Inventory, and audit trail configuration.

Tests understanding of automation tools and their trade-offs in complexity, maintenance, and performance.

Tests practical problem-solving: deduplication strategy, merge rules, data backup, validation, and prevention measures.

Look for phased approach: data mapping, ETL, parallel running, validation, cutover weekend, and rollback plan.

Tests client-side optimization skills — page layout simplification, automation cleanup, data archival, and Accounting-specific optimizations.

Assess practical Python proficiency — look for specific use cases, not just surface-level familiarity.

Look for integration patterns, error handling, data validation, and experience with REST/GraphQL APIs.

Reveals professionalism and efficiency — look for version control, code review, automation, and collaboration tools.

Tests analytical decision-making — should consider team familiarity, project requirements, long-term maintenance, and community support.