About the job
Join Our Team at CodeNinja
CodeNinja is at the forefront of AI-driven solutions, dedicated to empowering enterprises, government entities, and software buyers in crafting and managing intelligence-led systems tailored for mission-critical applications. Our expertise lies in seamlessly integrating AI into operational frameworks—leveraging robust engineering principles alongside AI-native methodologies to deliver tangible value, resilience, and sustainable ownership for our clients. With a global presence and diverse delivery strategies supported by AI Labs, AI Pods, and Global Capability Centers, we enable our teams to co-create scalable platforms across various regions and time zones.
Role Overview
As a pivotal player in our ERP program, you will spearhead the data strategy, ensuring comprehensive data readiness, effective conceptual data modeling, meticulous reporting data sourcing, and structured migration planning. Your efforts will cultivate a dependable, unified data foundation across all operational modules.
Key Responsibilities
- Conduct thorough evaluations of legacy data sources:
- Inventory, Maintenance (MRO), Procurement, HR, Technical Documentation
- Identify critical data quality issues:
- Duplicates, missing entries, inconsistencies
- Data ownership and governance deficiencies
- Redundant or siloed datasets
- Establish a robust data cleansing and standardization framework.
- Create a comprehensive Data Readiness Assessment Report.
- Formulate an end-to-end Data Migration Strategy, encompassing:
- Migration methodologies (phased, module-wise, or big-bang)
- Data prioritization (master, transactional, historical)
- Develop Data Mapping Specifications:
- Mapping from legacy systems to ERP field-level
- Transformation rules and standardization logic
- Strategies for handling missing or invalid data
- Define a Data Cleansing & Preparation Plan:
- Deduplication protocols
- Data enrichment processes as needed
- Validation checkpoints pre-migration
- Craft a Migration Execution Plan:
- Migration cycles (mock runs, dry runs, final cutover)
- Rollback strategies in case of failure
- Downtime and cutover planning
- Establish a Data Validation & Reconciliation Framework:
- Pre-migration vs post-migration validation rules
- Reconciliation reports (counts, totals, relationships)
- Sign-off criteria for each module
- Generate key deliverables:
- Data Migration Plan
- Data Mapping Documents
- Data Validation & Reconciliation Reports
- Identify and enable innovative AI/ML use cases, such as:
- Predictive maintenance modeling
- Inventory demand forecasting
- Supplier performance analytics
- Utilize AI tools for:
- Data profiling and anomaly detection
- Assessing data quality and recommending cleansing actions
- Advanced analytics and insights generation

