Data Architect – Dynamic Pricing & Decision Intelligence Platform✦ anywhere

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

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Define the end-to-end data architecture across batch, streaming, and real-time layers.

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Design Customer 360 and Offer 360 data models, including feature store and historical layers.

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Establish logical, physical, and semantic data models to enable segmentation, scoring, and orchestration.

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Define data contracts for Kafka topics, API payloads, and Adobe/CDP integrations.

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Set up data versioning and lineage frameworks to track data provenance

Data Ingestion & Integration

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Architect data ingestion pipelines from multiple telco sources: OCS, CRM, Kenan, Billing, DWH, Adobe AEP, Pricefx, and external APIs.

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Define patterns for real-time event ingestion (recharge, offer purchase, balance check).

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Standardize data access through APIs or data products for downstream modules.

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Design connectors for cloud storage (e.g., S3, Delta Lake) and integration middleware (e.g., n8n, , KNIME).

Data Management & Governance

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Define and enforce data quality, lineage, catalog, and access policies.

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Establish metadata management frameworks (e.g., DataHub, Collibra, Amundsen).

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Set up data validation and DQ frameworks (Great Expectations, Deequ).

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Govern data partitioning, schema evolution, retention, and archiving strategies.

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Ensure compliance with data privacy and regulatory standards (e.g., PDPA, GDPR, local telecom data policies).

Scalability, Cost & Performance

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Design for high performance and cost-efficient scalability (25M → 50M → 75M customers).

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Optimize compute/storage balance across environments (Dev, UAT, Prod).

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Define data lakehouse optimization strategies (Z-Order, Delta caching, compaction).

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Monitor and manage query performance, cluster sizing, and job orchestration costs.

Collaboration & Governance

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Work closely with Data Engineers, Data Scientists, and Application Developers to ensure architectural alignment.

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Lead architecture review boards and maintain data design documentation (ERD, flow diagrams, schema registry).

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Serve as technical liaison between business stakeholders, data teams, and platform vendors (Databricks, Adobe, Pricefx).

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Provide best practices and design patterns for model deployment, retraining, and data lifecycle management.

Requirements

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10+ years in Data Architecture and Data Platform Design

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Data Architecture-Data lakehouse design, data modeling (dimensional, normalized, semantic), schema management.

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ETL/ELT & Orchestration-Databricks, Snowflake, dbt, Airflow, AWS Glue, Azure Data Factory.

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Streaming & Real-Time-Apache Kafka, Spark Streaming, Kinesis, Flink

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Data Modeling-Customer 360, Offer 360, Transaction 360, Feature Store

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Cloud Platforms-AWS (S3, Glue, Lambda, EMR), Azure (ADF, Synapse), or GCP (BigQuery, Dataflow)

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Storage & Compute-Delta Lake, Parquet, Iceberg, Snowflake

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Data Quality & Governance-Great Expectations, Deequ, DataHub, Collibra

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Programming & Scripting-Python, SQL, PySpark, YAML

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API & Integration Design-REST, GraphQL, Kafka Connect, JSON schema

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Security & Compliance-IAM, encryption (KMS), access control, masking, PDPA compliance

Preferred (Nice-to-Have)

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Telecom industry experience (recharge, balance, offer, churn, usage data).

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Experience integrating with Adobe Experience Platform (AEP) or Pricefx.

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Knowledge of , KNIME, or n8n for workflow orchestration.

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Familiarity with AI/ML pipelines and MLOps frameworks (MLflow, SageMaker).

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Exposure to knowledge graphs (Neo4j, GraphFrames) for segmentation and recommendation.

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Educational Background Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related fields.

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Certifications in AWS/Azure Data Architect, Databricks Certified Data Engineer, or Snowflake Architect are preferred.

Highlights
Location: Remote Experience
Department: Data & AI Engineering

Originally posted on Himalayas