Data Modeling Engineer RRS & Migration

Position Overview

Click Global Talent is recruiting a Data Modeling Engineer with expertise in Regulatory Reporting System (RRS) and data migration for a leading organisation. This is a full-time, program-based, multi-year remote engagement. The role involves a phased delivery approach, from analysis and design through build, SIT, UAT, parallel run, and go-live, with the engineer engaged throughout the entire lifecycle, including reconciliation and cutover. You will work closely with business analysts, ETL/ELT developers, DBAs, and the data architect. Mandarin-speaking candidates only.

Key Responsibilities

• Data Modelling: Produce and maintain conceptual, logical, and physical data models for staging, integration, and consumption layers. Apply dimensional, 3NF, and Data Vault techniques, defining grain, keys, relationships, history, and naming standards. Review and govern models for consistency, reusability, and conformance to target architecture.

• RRS Model Conversion, Enhancement & New-Build: Convert legacy RRS data models to the target platform, preserving regulatory calculations and reporting outputs. Enhance existing RRS models for new platform or regulatory requirements without breaking existing submissions. Build new RRS models for reporting requirements not met by the legacy estate, ensuring support for BNM / PIDM regulatory submissions with correct lineage and auditability.

• Point-to-Point Migration Modelling & Schema Conversion: Perform 1:1 conversion of legacy objects (tables, views, structures) into the target DWS, translating Netezza data types, DDL, and constraints to GaussDB/DWS equivalents. Preserve structure, grain, and output exactly for like-for-like migrations, documenting any unavoidable deviations. Define and specify schema-conversion rules and reusable patterns.

• MPP Physical Design & Performance: Design physical models for the target MPP engine, including distribution/hash keys, partitioning, compression, and storage layout for optimal query and batch performance. Advise ELT developers on set-based, push-down-friendly model structures.

• Source-to-Target Mapping & Lineage: Produce Source-to-Target Mapping (STTM) specifications with transformation, derivation, and data-type conversion rules. Maintain data lineage and metadata for models in governance/catalogue tooling.

• Reconciliation & Testing Support: Define model-level reconciliation and validation rules (row counts, control totals, balances) between legacy and target, and support SIT, UAT, and parallel-run reconciliation. Investigate and resolve model-related defects and mismatches to root cause.

• Collaboration & Standards: Work closely with business analysts, ETL/ELT developers, DBAs, and the data architect, translating requirements and BRDs into buildable models. Maintain modelling standards, a data dictionary, and model documentation.

Key Deliverables

• Conceptual, logical, and physical data models for RRS and migrated subject areas, signed off per stage gate.

• Target DDL and schema-conversion specifications (Netezza → GaussDB/DWS), including reusable conversion patterns.

• Converted, enhanced, and net-new RRS models across staging / pre-base / base / reporting layers.

• Source-to-Target Mapping (STTM) specifications with data-type and transformation rules.

• MPP physical-design decisions (distribution, partitioning) documented per model.

• Model-level reconciliation / validation rules and support through SIT, UAT, and parallel run.

• Model documentation, data dictionary, and lineage entries.

Required Qualifications & Experience

• Minimum 8 years in data modelling / data engineering, with substantial time on enterprise data warehouse and data-migration programmes.
• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related discipline.
• Deep, hands-on data modelling across conceptual, logical, and physical layers, using dimensional, 3NF, and Data Vault techniques.
• Proven delivery of a platform migration — ideally MPP-to-MPP (e.g., Netezza, Teradata → GaussDB/DWS, Snowflake) — including schema/DDL conversion and data-type translation.
• Experience with point-to-point / functional-equivalence (like-for-like) migration where structures and results must be preserved exactly.
• Experience modelling for regulatory reporting (RRS or equivalent BNM/central-bank reporting estates) is strongly preferred.
• Strong, practical SQL and DDL; able to write and validate DDL, profile data, and build reconciliation queries against large datasets independently.
• Experience producing source-to-target mappings that engineering can build from without rework.
• Experience across the full delivery lifecycle: design, build, SIT, UAT, parallel run, and cutover.
• Banking / financial-services background; excellent written and spoken English (Bahasa Malaysia an advantage).

Technical Skills & Tools

• Modelling methods: Dimensional (Kimball), normalised (Inmon/3NF), Data Vault 2.0; slowly-changing dimensions, grain and key design.
• Modelling tools: erwin, ER/Studio, SQLDBM, PowerDesigner, or equivalent.
• Platforms: Hands-on with IBM Netezza (source) and Huawei GaussDB / DWS (target); familiarity with other MPP/columnar warehouses (Teradata, Snowflake, BigQuery) a plus.
• SQL / DDL & schema conversion: Advanced SQL; writing target DDL; translating data types, constraints, stored logic, and distribution/partition strategy from Netezza to GaussDB/DWS.
• ELT / integration: Designing models for push-down ELT; working knowledge of an enterprise ELT/orchestration tool (e.g., Sunline DataMind, Informatica, DataStage, Talend).
• Performance: MPP physical design — distribution/hash keys, partitioning, compression, query tuning concepts.
• Reconciliation & validation: Control-total, checksum, and row-count techniques; advanced Excel; Python/scripting for validation a plus.
• Governance: Metadata, lineage, business glossary, and data-catalogue concepts; version control for models (Git or tool-native).

Domain Knowledge

• Regulatory reporting (RRS): Understanding of a regulatory reporting data estate and its typical staging / pre-base / base / reporting layers, parameter-driven reporting, and the need for traceable, auditable lineage end to end.
• Malaysian regulatory context: BNM and PIDM reporting obligations; prior exposure to submissions such as statutory/credit/deposit-insurance reporting is a strong advantage.
• Banking data domains: Core banking, deposits, loans/financing, cards, treasury, general ledger, and customer/party data.
• Migration discipline: Functional-equivalence principles, data reconciliation, and controls expected when migrating regulated data.

Core Competencies

• Precise and detail-driven — treats functional equivalence and reconciliation as non-negotiable, and documents every deviation.
• Strong analytical and problem-solving skills; can reverse-engineer undocumented legacy structures and reason about target design.
• Able to work independently on a model workstream and collaborate closely with BAs, engineers, and DBAs.
• Pragmatic about performance vs purity — makes sound trade-offs on the target MPP platform.
• Communicates model decisions clearly to both technical and business stakeholders; flags risks and gaps early.

Preferred Qualifications

• Prior Netezza-to-GaussDB/DWS (or comparable MPP-to-MPP) schema-conversion and migration experience.
• Hands-on Data Vault 2.0 modelling for an integration layer.
• Experience delivering a BNM / PIDM regulatory-reporting (RRS) data model or migration.
• Certification such as CDMP (DAMA), a vendor data-modelling certification, or a recognised cloud-data-warehouse certification.
• Exposure to Sunline DataMind, Huawei DWS/GaussDB tooling, or similar ELT/governance platforms.
• Experience with model version control and automated DDL deployment.