Senior Data Manger

Bitexco Group is executing Project BC-X, a group-wide digital transformation program that unifies Grade A commercial office (Bitexco Financial Tower), premium retail (The Garden Shopping Mall), luxury real estate, hospitality (JW Marriott Hanoi, Melia Ba Vi), education (Dwight School Hanoi), cultural attractions (L49 Skydeck, Ao Dai Museum), and energy (Connected Plants) into a single physical-digital ecosystem. The backbone is the Digital Business Operating System (DBOS), an event-driven B2B2C orchestration layer running on a three-tier ERP architecture.

The Senior Data & AI Manager owns the Group's data and AI capability inside the BC-X Data & AI Center of Excellence (CoE). The mandate is to design and operate the Lakehouse, the ERP, CDP, the MDM layer, and the enterprise AI platform that the B2B2C commerce platform, the loyalty and CRM stack, and every business unit's operational systems will run on for the next decade. The role reports to the CXO on strategy and architecture and to the CEO/BOD on delivery and financial ROI, and it sits as a peer to the Product Engineering, BA, and PMO pillars of BC-X.

Key Responsibilities

1. Data & AI Strategy and Governance

- Define and own the Group's Data & AI strategy as an integrated layer of the DBOS. Every new data domain should map onto the existing three-tier ERP and B2B2C architecture, keeping a single reconciled source of truth.

- Design and own a 360-degree Customer Data Platform spanning real estate, retail, hospitality, education, and office, with an explicit B2B partner data model that closes the gap identified in the current B2B2C platform RFP review.

- Own Master Data Management standards as a horizontal layer beneath the CDP and loyalty engine, covering Customers, Partners, Digital Twin Properties, and operational metrics, in line with the Group's MDM-first architecture principle.

- Design a dynamic, fintech-grade loyalty and points model with double-entry ledger discipline, cross-BU point matrices, and B2B/B2C profiling, built to reconcile cleanly against the Tier 1 ERP.

- Implement data quality, lineage, and security controls that meet SOC 2 and PCI-DSS, alongside Vietnam-specific obligations: 13/2023 for personal data protection, Land Law 2024 for real estate and Digital Twin property data, and TT78/2021 for e-invoicing.

2. Data & AI Platform Architecture

- Design and direct a cloud-based Data Lakehouse (AWS/S3, Delta Lake) with standard Landing/Bronze/Silver/Gold layering, sized and costed against a realistic 5-year TCO.

- Build bidirectional pipelines (AWS Glue, dbt, Airflow) connecting SAP S/4HANA (FICO, MM, SD, PS, RE-FX), Bravo/MISA at Tier 2, retail POS, hospitality PMS, CRM, and BMS/SCADA IoT systems, with reconciliation logic that respects the Group's intercompany elimination rules.

- Own the semantic and serving layer (StarRocks, Cube.dev, Redshift/Athena as applicable) so that BU-level BI, the NL2SQL/conversational BI layer, and the AI platform all read from one governed model.

- Direct a secure LLM Gateway with PII masking, RAG pipelines against a Vector Database and Enterprise Knowledge Graph, and Multi-Agent systems for the physical-digital use cases already in scope: e-contract drafting and review for leasing, energy demand forecasting, and hospitality room yield optimization.

- Design access control and encryption (AES-256 at rest, TLS 1.3 in transit, AWS Lake Formation role-based access) as a direct extension of the Group's Zero Trust/IAM architecture under NIST SP 800-207.

3. CoE Leadership and Delivery Governance

- Recruit and lead Agile squads (Data Engineers, Data Scientists, AI Engineers, BI Analysts, MLOps Engineers) inside the Data & AI CoE, coordinating closely with the Product Engineering, BA, and PMO pillars.

- Shift the organization from pull-based static dashboards toward push-based insight delivery: NL2SQL, conversational BI, and early-warning systems for operational metrics such as occupancy, footfall, energy demand, and collections.

- Enforce MLOps/DevSecOps discipline (model versioning, drift detection, testing) at a level the CoE can realistically sustain given its size, scaling the rigor as the team grows.

- Approve UAT standards for production releases and hold final sign-off on Master Data Schema, ETL/ELT pipeline design, and semantic layer modeling before CXO submission.

4.Candidate Profile

- Bachelor's degree in Computer Science, AI, Data Science, IT, or a related field. A Master's, or MBA/executive management qualification strengthens a candidacy without being required.

- A minimum of 7 years in data and analytics roles, including at least 4 years in a senior leadership capacity (Head of Data, Data Architecture Lead, or equivalent) with direct accountability for a production Lakehouse or enterprise data platform.

- At least 5 years of hands-on experience with AI/ML or Generative AI systems running in production and carrying real transaction or operational volume.

- Demonstrated experience integrating a modern data stack with a live SAP S/4HANA (or equivalent ERP) environment, covering at least one of FICO, MM, SD, PS, or RE-FX, CRM - CDP system.

- Prior experience in a multi-industry conglomerate, major bank, or comparable environment where a single CDP or Lakehouse served several genuinely distinct business models under one architecture.

- Experience standing up or scaling a Data & AI CoE from a small founding team, including the hiring and people-management work that comes with it.

- Willing to hold a firm architectural line with vendors and business units when a proposed shortcut would fragment the single customer/partner profile the Group is building.

Technical Expertise: Candidate does not need every item below, but should be able to speak fluently to most of each layer and name what they have actually run in production.

• Ingestion, CDC, and event streaming (feeds the DBOS event backbone)
• Kafka or AWS MSK, AWS Kinesis, Debezium for change-data-capture off SAP and POS/PMS databases, Fivetran or Airbyte for SaaS connectors, AWS DMS for database migration and replication, MQTT and OPC-UA for BMS/SCADA telemetry.
• Lakehouse, storage, and processing: AWS S3, Delta Lake, Apache Iceberg as an alternative table format worth knowing even if the Group standardizes on Delta, AWS Glue and EMR/Spark for large-scale transforms, dbt for the transformation and testing layer, Airflow or Dagster for orchestration.
• Warehousing and semantic/serving layer: Redshift, Athena, StarRocks, Cube.dev, Snowflake or BigQuery as points of comparison during architecture reviews even if not selected, Power BI, Tableau, or Apache Superset for the BI consumption layer.
• Master data, catalog, and lineage: SAP Master Data Governance (SAP MDG) given the S/4HANA core, Informatica MDM or Reltio as platform-agnostic alternatives, DataHub, OpenMetadata, or Collibra for catalog and lineage, Great Expectations or Monte Carlo for data quality monitoring, Senzing or Tamr for entity resolution across the five verticals' customer and partner records.
• CDP, CRM, and loyalty:Segment, Adobe Real-Time CDP, or a Lakehouse-native CDP built on the reverse-ETL pattern (Hightouch, Census), Salesforce or SAP CX/C4C for CRM, a custom or vendor loyalty ledger capable of double-entry point accounting.
• AI and LLM platform: AWS Bedrock, Azure OpenAI Service, or Vertex AI for model hosting, LiteLLM or Kong AI Gateway for the LLM Gateway and routing layer, LangChain, LlamaIndex, or LangGraph for RAG and agent orchestration, pgvector, OpenSearch, Pinecone, or Milvus for the vector database, Neo4j or Amazon Neptune for the Enterprise Knowledge Graph, Amazon Q, Microsoft Copilot, or SAP Joule for embedded NL2SQL and conversational BI where the ERP vendor already ships an agent layer.
• Identity, security, and DevSecOps: AWS IAM Identity Center, Okta, or Azure Entra ID for Zero Trust/IAM, infrastructure as code, AWS Macie or Protegrity for PII discovery and masking.

Reporting and Governance

- Reports directly to the CXO on strategy, AI roadmap, and system architecture; reports periodically to the CEO and BOD on delivery progress, budget, and financial ROI.

- Works as a peer to the BA leads, PMO, and Product Engineering pillars within TechHub, and directly with BU Heads across Real Estate, Commercial Office, Retail, Hospitality, Education, and Energy.

- Holds final technical approval over Master Data Schema, ETL/ELT pipelines, and semantic layer modeling prior to CXO submission, and approves UAT standards for production releases.