Data Architect – Dynamic Pricing & Decision Intelligence Platform

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Role Summary

The Data Architect will design and govern the complete data ecosystem for the ValueX platform — including data ingestion, processing, modeling, storage, orchestration, and governance.

They will define the data architecture blueprint supporting the system’s core modules.

1. Customer Segmentation
2. Business Decisioning & Offer Assignment Engine
3. Real-Time Offer Orchestration Module (ROOM)
4. ML/AI Model Management & Simulation Engine

The architect will ensure scalability to handle 50 million+ customer profiles, real-time event streams, and ML-driven decisioning — with a focus on performance, cost optimization, and maintainability.

Key Responsibilities

Data Architecture & Design

• Define the end-to-end data architecture across batch, streaming, and real-time layers.
• Design Customer 360 and Offer 360 data models, including feature store and historical layers.
• Establish logical, physical, and semantic data models to enable segmentation, scoring, and orchestration.
• Define data contracts for Kafka topics, API payloads, and Adobe/CDP integrations.
• Set up data versioning and lineage frameworks to track data provenance

Data Ingestion & Integration

• Architect data ingestion pipelines from multiple telco sources: OCS, CRM, Kenan, Billing, DWH, Adobe AEP, Pricefx, and external APIs.
• Define patterns for real-time event ingestion (recharge, offer purchase, balance check).
• Standardize data access through APIs or data products for downstream modules.
• Design connectors for cloud storage (e.g., S3, Delta Lake) and integration middleware (e.g., n8n, DecisionRules.io , KNIME).

Data Management & Governance

• Define and enforce data quality, lineage, catalog, and access policies.
• Establish metadata management frameworks (e.g., DataHub, Collibra, Amundsen).
• Set up data validation and DQ frameworks (Great Expectations, Deequ).
• Govern data partitioning, schema evolution, retention, and archiving strategies.
• Ensure compliance with data privacy and regulatory standards (e.g., PDPA, GDPR, local telecom data policies).

Scalability, Cost & Performance

• Design for high performance and cost-efficient scalability (25M → 50M → 75M customers).
• Optimize compute/storage balance across environments (Dev, UAT, Prod).
• Define data lakehouse optimization strategies (Z-Order, Delta caching, compaction).
• Monitor and manage query performance, cluster sizing, and job orchestration costs.

Collaboration & Governance

• Work closely with Data Engineers, Data Scientists, and Application Developers to ensure architectural alignment.
• Lead architecture review boards and maintain data design documentation (ERD, flow diagrams, schema registry).
• Serve as technical liaison between business stakeholders, data teams, and platform vendors (Databricks, Adobe, Pricefx).
• Provide best practices and design patterns for model deployment, retraining, and data lifecycle management.