Data Architect

HTC Global Services; Chennai District; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

HTC Global Services in Chennai is seeking an 8+ year Data Architect with deep expertise in Azure Data Platform, Databricks, ADF and PySpark to design enterprise data solutions.

You will shape data lakes and data warehouses, implement Medallion Architecture, build scalable ETL/ELT pipelines, optimize Spark workloads, and enable advanced analytics and ML workloads. The role focuses on cloud-native data architectures using Azure services, data ingestion, transformation, and orchestration pipelines.

Qualifications

• 8+ years of experience in enterprise data architecture and design.
• Strong hands-on experience with Azure Data Platform and Databricks.
• Proficient in PySpark, Spark SQL, Delta Lake and Lakehouse concepts.

Responsibilities

• Design and implement enterprise data architecture including data lakes, data warehouses, and Lakehouse solutions.
• Define and maintain enterprise data models, data standards, and architectural best practices.
• Design and develop data processing solutions using Azure Databricks.
• Build scalable ETL/ELT pipelines with PySpark and Delta Lake.
• Optimize Spark workloads for performance, cost, and scalability.
• Implement Medallion Architecture (Bronze, Silver, Gold) and Lakehouse best practices.
• Support advanced analytics and machine learning workloads within Databricks.
• Architect and implement cloud-native data solutions using Microsoft Azure services.
• Design data ingestion, transformation, and orchestration pipelines using Azure Data Factory.

Skills

Azure

Databricks

ADF

PySpark

Lakehouse

Medallion Architecture

Spark SQL

Delta Lake

ETL/ELT

Data Lakes/Warehouses

Job description

Job Description

About the Role

We are seeking a Data Architect with 8 years of experience in Azure Data Architect, Databricks, ADF and Pyspark

Requirements

• Design and implement enterprise data architecture, including data lakes, data warehouses, and Lakehouse solutions.


• Define and maintain enterprise data models, data standards, and architectural best practices.


• Design and develop enterprise data processing solutions using Azure Databricks.


• Build scalable ETL/ELT pipelines using PySpark, Spark SQL, and Delta Lake.


• Optimize Spark workloads for performance, cost, and scalability.


• Implement Medallion Architecture (Bronze, Silver, Gold) and Lakehouse best practices.


• Support advanced analytics and machine learning workloads within Databricks.


• Architect and implement cloud-native data solutions using Microsoft Azure services.


• Design data ingestion, transformation, and orchestration pipelines using Azure Data Factory (ADF).