CodeNinja is a full-stack AI delivery company that helps enterprises, governments, and software acquirers build and operate intelligence-driven systems for mission-critical workflows. We specialize in deploying AI into real operations—combining strong engineering fundamentals with AI-native delivery to create measurable value, resilience, and long-term ownership for our clients. Our global footprint and delivery model are supported by AI Labs, AI Pods, and Global Capability Centers, enabling teams to co-engineer scalable platforms across regions and time zones.
Lead the ERP program’s data strategy by ensuring data readiness, conceptual data modelling, reporting data sourcing, and structured data migration planning, enabling a reliable, unified data foundation across all modules.
· Conduct comprehensive assessment of legacy data sources:
o Inventory, Maintenance (MRO), Procurement, HR, Technical Docs
· Identify:
o Data quality issues (duplicates, missing, inconsistencies)
o Data ownership and governance gaps
o Redundant/siloed datasets
· Define data cleansing and standardization approach
· Produce Data Readiness Assessment Report
· Define end-to-end Data Migration Strategy, including:
o Migration approach (phased, module-wise, or big-bang)
o Data prioritization (master data vs transactional vs historical)
· Develop Data Mapping Specifications:
o Legacy → ERP field-level mappings
o Transformation rules and standardization logic
o Handling of missing/invalid data
· Define Data Cleansing & Preparation Plan:
o Deduplication rules
o Data enrichment where required
o Validation checkpoints before migration
· Design Migration Execution Plan:
o Migration cycles (mock runs, dry runs, final cutover)
o Rollback strategy in case of failure
o Downtime and cutover planning
· Define Data Validation & Reconciliation Framework:
o Pre-migration vs post-migration validation rules
o Reconciliation reports (counts, totals, relationships)
o Sign-off criteria for each module
· Produce key deliverables:
o Data Migration Plan
o Data Mapping Documents
o Data Validation & Reconciliation Reports
· Identify and enable AI/ML use cases, such as:
o Predictive maintenance modeling
o Inventory demand forecasting
o Supplier performance analytics
· Leverage AI tools for:
o Data profiling and anomaly detection
o Data quality assessment and cleansing recommendations
o Advanced analytics and insights generation
· Define enterprise conceptual data model:
o Assets, Inventory, Work Orders, Suppliers, Workforce, Technical Docs
· Establish:
o Entity relationships
o Master data definitions
o Data standardization rules
· Support BI teams for dashboards and analytics
· Align data models with business workflows
· Ensure requirements are data-feasible and consistent
Requirements
3. Required Skills & Experience
Mandatory
· 5 - 8 years in Data Analyst / Data Architecture roles
· Experience in large-scale ERP or enterprise data transformation programs
· Strong expertise in:
o Data modelling (conceptual & logical)
o Data migration and cleansing
o Data warehousing and reporting
Technical Expertise
· Strong knowledge of:
o ETL processes and migration tools
o SQL and relational databases
o API-based integrations
· Experience with BI tools (Power BI, Tableau)
Preferred
· Experience in ERP & Enterprise Data Management (logistics, manufacturing)
· Exposure to:
o Maintenance (MRO) data
o Inventory systems
o Technical documentation repositories
· Demonstrated experience using AI/ML tools or platforms, such as:
o Data analysis and prediction tools
o AI-assisted data modeling or profiling tools
· Ability to translate data into AI-driven insights for decision-making
We are an equal opportunity employer and are committed to fostering an inclusive, diverse, and equitable workplace. We do not discriminate based on race, religion, gender, age, disability, nationality, marital status, or any other protected characteristic under applicable law. We encourage candidates from all backgrounds to apply.
Benefits