Data Modeling Engineer

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