Data Architect - Microsoft Azure Data Services, DataLake, Databricks

HireOn; Pakistan; Hybrid; PKR 3,000,000 - 5,500,000; Full time

Job summary

HireOn is seeking a seasoned Data Architect to design and govern Azure-based data platforms, pipelines, and lakehouse architectures. You will drive scalable solutions for analytics, BI, AI, and enterprise reporting, collaborating with stakeholders across business units.

The role requires 10+ years of experience in data engineering, warehousing, and cloud-native architectures, with hands-on expertise in Azure Data Factory, Synapse, Databricks, and Python/Scala.

Qualifications

• 10+ years of experience in Data Engineering, Data Warehousing, and Enterprise Data Platform development.
• Strong hands‑on experience with Microsoft Azure Data Services.
• Expertise in Azure Data Factory (ADF), Azure Synapse Analytics, Azure Data Lake Storage (ADLS Gen2), and Azure SQL Database.
• Experience building and managing large-scale ETL/ELT pipelines and data integration solutions.
• Strong proficiency in SQL, query optimization, and database performance tuning.
• Hands‑on experience with PySpark, Apache Spark, and distributed data processing frameworks.
• Strong programming skills in Python, Scala, or Java.
• Experience with dimensional modeling, data warehousing concepts, and modern lakehouse architectures
• Experience working with structured, semi-structured, and unstructured data.
• Strong understanding of data governance, data quality, metadata management, and security best practices.
• Experience with REST APIs, data integration patterns, and enterprise system connectivity.
• Hands‑on experience with Git, CI/CD pipelines, and DevOps practices.
• Strong analytical, problem‑solving, and communication skills.

Responsibilities

• Design, develop, and maintain scalable data platforms and data pipelines on Microsoft Azure.

Skills

Data Engineering

Data Warehousing

Azure Data Services

SQL

PySpark

Apache Spark

Python

Scala

Java

ETL/ELT pipelines

Tools

Azure Data Factory

Azure Synapse Analytics

Azure Data Lake Storage

Azure SQL Database

Azure Databricks

Delta Lake

Power BI

Job description

Data Architect - Azure Data Engineering, DataLake, PySpark, Databricks

Location: PK
Employment Type: Full-Time, Permanent

Work Model: Mostly remote, but it can be one-day-work-from-office as well

Job Summary

We are seeking an experienced Data Architect with 10+ years of experience in designing, developing, and managing modern data platforms, data pipelines, and cloud-based analytics solutions. The ideal candidate will have strong expertise in Azure Data Services, large-scale data processing, data warehousing, ETL/ELT frameworks, and cloud-native data architectures. The role requires hands‑on experience in building scalable, secure, and high-performance data solutions that support enterprise analytics, reporting, AI, and business intelligence initiatives.

Key Responsibilities

• Design, develop, and maintain scalable data platforms and data pipelines on Microsoft Azure.
• Build and optimize batch and real‑time data ingestion frameworks from multiple structured and unstructured data sources.
• Design and implement data lake, data warehouse, and lakehouse architectures to support analytics and reporting workloads.
• Develop and manage ETL/ELT processes using modern cloud‑native data engineering practices.
• Implement data transformation, cleansing, validation, and quality frameworks to ensure data accuracy and reliability.
• Collaborate with business stakeholders, data analysts, data scientists, and application teams to understand data requirements and deliver scalable solutions.
• Optimize data storage, processing, and query performance across enterprise data platforms.
• Implement security, governance, monitoring, and compliance best practices across Azure environments.
• Support integration of data platforms with AI/ML, business intelligence, and enterprise applications.
• Participate in architecture reviews, code reviews, troubleshooting, and technical mentoring activities.
• Ensure high availability, scalability, and operational excellence of data platforms and pipelines.

Required Skills & Qualifications

• 10+ years of experience in Data Engineering, Data Warehousing, and Enterprise Data Platform development.
• Strong hands‑on experience with Microsoft Azure Data Services.
• Expertise in Azure Data Factory (ADF), Azure Synapse Analytics, Azure Data Lake Storage (ADLS Gen2), and Azure SQL Database.
• Experience building and managing large-scale ETL/ELT pipelines and data integration solutions.
• Strong proficiency in SQL, query optimization, and database performance tuning.
• Hands‑on experience with PySpark, Apache Spark, and distributed data processing frameworks.
• Strong programming skills in Python, Scala, or Java.
• Experience with dimensional modeling, data warehousing concepts, and modern lakehouse architectures.
• Experience working with structured, semi-structured, and unstructured data.
• Strong understanding of data governance, data quality, metadata management, and security best practices.
• Experience with REST APIs, data integration patterns, and enterprise system connectivity.
• Hands‑on experience with Git, CI/CD pipelines, and DevOps practices.
• Strong analytical, problem‑solving, and communication skills.

Preferred Qualifications

• Experience with Microsoft Fabric, OneLake, Dataflows, and Fabric Data Engineering workloads.
• Experience with Databricks, Delta Lake, and lakehouse implementations.
• Knowledge of real‑time streaming technologies such as Azure Event Hubs, Apache Kafka, or Azure Stream Analytics.
• Experience supporting AI/ML and advanced analytics workloads through enterprise data platforms.
• Familiarity with Power BI datasets, semantic models, and enterprise reporting architectures.
• Experience with data governance tools such as Microsoft Purview.
• Microsoft Azure Data Engineering certifications are highly preferred.
• Experience working in Agile/Scrum environments.

Nice to Have

• Experience with Microsoft Fabric Data Engineering and Analytics solutions.
• Exposure to MLOps and DataOps practices.
• Knowledge of containerization technologies such as Docker and Kubernetes.