AVP, Data Architecture

Key Responsibilities

Data Architecture & Design

• Define and maintain enterprise-wide data architecture blueprints (logical, physical, and conceptual models)
• Design scalable data platforms including data lakes, data warehouses, lakehouses, and streaming architectures
• Establish standards for data modeling, integration, storage, and access
• Develop data architecture aligned with cloud (Azure, AWS, GCP) and hybrid environments

2. Data Strategy & Governance

• Partner with leadership to define data strategy, roadmap, and operating model
• Establish and enforce data governance frameworks including data quality, metadata, lineage, and stewardship
• Ensure compliance with regulatory requirements (e.g., PDPA, GDPR where applicable)

3. Data Integration & Engineering

• Design and oversee data pipelines (batch and real-time)
• Define patterns for data ingestion, transformation, and orchestration
• Enable integration across enterprise systems (ERP, CRM, Core, digital platforms)

4. Analytics & AI Enablement

• Architect data platforms that support BI, advanced analytics, and AI/ML workloads
• Enable self-service analytics through governed data access
• Collaborate with data scientists and analysts to optimize data usability

5. Technology & Innovation

• Evaluate and recommend data technologies, tools, and platforms
• Champion modern architecture (e.g., data mesh, lakehouse, event-driven architecture)
• Drive automation, scalability, and performance optimization

6. Stakeholder Engagement

• Collaborate with business, IT, and leadership stakeholders to translate requirements into data solutions
• Provide technical leadership and mentorship to data engineers and architects
• Communicate architecture decisions and trade-offs effectively

Requirement

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or related field
• 8–12+ years of experience in data management, architecture, or engineering
• Proven experience designing enterprise-scale data architecture
• Strong knowledge of:

1. Data modeling (Kimball, Inmon, Data Vault)
2. ETL/ELT processes and tools
3. SQL, NoSQL databases
4. Big data technologies (e.g., Spark, Hadoop, Snowflake, Databricks)

• Hands-on experience with cloud data platforms (Azure Synapse, Databricks, Snowflake, BigQuery)
• Knowledge of data governance tools (e.g., Collibra, Purview, Alation)
• Familiarity with streaming platforms (Kafka, Event Hub)
• Experience with DevOps/DataOps practices
• Understanding of API-based and microservices architectures