Data Architect - Microsoft Azure Data Services, DataLake, Databricks

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.