Azure Data Platform Lead - Cloud DW, Databricks & BI

GMP Technologies; Singapore; On-site; SGD 180,000 - 230,000; Full time

Job summary

GMP Technologies is seeking an Azure Data Lead to drive migration planning and the design of a cloud-based data warehouse with Databricks and Power BI in a banking context. You will oversee data ingestion, processing, modeling, and analytics to deliver actionable insights for business stakeholders.

The role requires strong leadership, 10+ years in data warehousing/BI, and deep knowledge of Azure data services, Databricks, and modern data architectures.

Qualifications

• Minimum 10 years in Data Warehousing, Analytics, Real-time Data Integration or BI.
• At least 3 years as Data Architect, Solution Architect or Technical Lead.
• 2+ years leading Azure data platform implementation teams.
• Proficiency in Databricks and modern data architectures (SQL, Hadoop, Spark, Kafka).

Responsibilities

• Plan on-premises system migration and cloud data warehousing strategy.
• Provide technical leadership in data access, ingestion, processing, modeling, and visualization.
• Collaborate with PMs to estimate tasks and deliverables; develop best practices and naming standards.

Skills

Data warehousing

Analytics

Real-time data integration

Business intelligence

Azure data platform

Databricks

SQL

Hadoop

Spark

Kafka

Power BI

QlikView

R

Python

Leadership

Education

Bachelor's degree in information technology, Computer Science, Manage Information Science, Banking and Finance or equivalent

Tools

Synapse

Data Factory

Event Hub

Azure SQL

Data Lake Gen2

Power BI

Job description

GMP Technologies is seeking an Azure Data Lead to drive migration planning and the design of a cloud-based data warehouse with Databricks and Power BI in a banking context. You will oversee data ingestion, processing, modeling, and analytics to deliver actionable insights for business stakeholders.

The role requires strong leadership, 10+ years in data warehousing/BI, and deep knowledge of Azure data services, Databricks, and modern data architectures.