Data Architect - Dynamic Pricing & Decision Intelligence Platform

At TechBiz Global , we are providing recruitment service to our TOP clients from our portfolio. We are currently seeking an

Data Architect to join one of our clients ' teams. If you're looking for an exciting opportunity to grow in a innovative environment, this could be the perfect fit for you.

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