Principal AWS Data Architect

HireOn; Lahore; Hybrid; PKR 3,500,000 - 7,000,000; Full time

Job summary

HireOn is seeking a Principal AWS Data Architect to drive the technical direction of enterprise data engineering and cloud data platform initiatives. The role suits an experienced data professional who can combine architectural expertise with practical AWS knowledge to build reliable, secure, and scalable data ecosystems.

The successful candidate will own data platform architecture, guide engineering implementation, and ensure data solutions meet governance, security, and business requirements.

Qualifications

• At least 12 years of experience across data engineering, data architecture, or large-scale data platform development.
• Strong portfolio of enterprise data solutions built on AWS.
• Experience designing complex data pipelines and distributed processing workflows.
• Ability to make architecture decisions across storage, processing, integration, governance, and security.

Responsibilities

• Develop enterprise data platform blueprints and architecture patterns for scalable AWS-based environments.
• Design robust data ingestion, transformation, and processing workflows for high-volume datasets.
• Build and optimize ETL/ELT solutions using AWS Glue, EMR, Lambda, and MWAA.
• Develop orchestrated data workflows and dependency management with Apache Airflow.

Skills

Data architecture

Enterprise data platforms

Data governance

Cloud data engineering

Leadership

Tools

AWS Glue

Amazon EMR

AWS Lambda

Apache Airflow (MWAA)

Amazon S3

Amazon Redshift

DynamoDB

AWS DataZone

EC2

CloudWatch

Job description

Recruitment Partner: HireOn

Location: Lahore / Karachi / Islamabad, Pakistan

Work Arrangement: Hybrid

Job Type: Full-Time

Required Experience: 12+ Years

Position Overview

HireOn is hiring a Principal AWS Data Architect on behalf of its client to drive the technical direction of enterprise data engineering and cloud data platform initiatives. This position is suited to an experienced data professional who can combine architectural expertise with practical knowledge of AWS technologies to build reliable, secure, and scalable data ecosystems.

The successful candidate will take ownership of data platform architecture, guide engineering implementation, and ensure data solutions meet performance, governance, security, and business requirements.

Key Areas of Responsibility

• Cloud Data Architecture: Develop enterprise data platform blueprints and define architecture patterns for scalable AWS-based data environments.
• Data Pipeline Engineering: Design robust data ingestion, transformation, integration, and processing workflows for high-volume datasets.
• AWS Processing Frameworks: Build and optimize ETL and distributed processing solutions using AWS Glue, Amazon EMR, and AWS Lambda.
• Workflow Automation: Develop orchestrated data workflows, scheduling mechanisms, and dependency management using Amazon MWAA (Apache Airflow).
• Data Storage & Analytics: Architect data lake and warehouse solutions using Amazon S3 and Amazon Redshift, selecting appropriate storage and processing patterns for analytical workloads.
• Data Modeling: Establish effective data models for relational, dimensional, analytical, and NoSQL use cases, including Amazon DynamoDB.
• Governance & Data Discovery: Implement metadata management, data cataloging, discovery, and governance practices using AWS Glue Data Catalog and AWS DataZone.
• Security & Compliance: Define access policies, identity controls, encryption standards, and secure data handling practices across cloud data environments.
• Platform Reliability: Establish monitoring, logging, and operational visibility using Amazon CloudWatch, while improving system resilience and maintainability.
• Performance & Cost Management: Identify bottlenecks, optimize processing workloads, and manage AWS resource utilization to improve efficiency and control costs.
• Architecture Leadership: Review technical designs, establish engineering standards, evaluate architectural trade-offs, and mentor data engineering teams.
• Platform Transformation: Lead data migration, cloud modernization, and architecture improvement initiatives in line with evolving business needs.

Technical Expertise Required

AWS Technology Stack: AWS Glue and Glue Data Catalog, Amazon EMR, AWS Lambda, Amazon MWAA (Apache Airflow), Amazon S3, Amazon Redshift, Amazon DynamoDB, AWS DataZone, Amazon EC2, and Amazon CloudWatch.

Data Engineering & Architecture: Enterprise data platform design, data lake and data warehouse architecture, lakehouse concepts, ETL/ELT development, data ingestion, distributed processing, workflow orchestration, advanced SQL, data transformation, dimensional modeling, and data quality management.

Cloud Engineering & Governance: AWS IAM, access control, encryption, cloud security, metadata management, data governance, monitoring, troubleshooting, workload optimization, and cloud cost management.

Professional Background

Candidates Should Demonstrate

• At least 12 years of experience across data engineering, data architecture, or large-scale data platform development.
• A strong portfolio of enterprise data solutions built on AWS.
• Practical experience designing complex data pipelines and distributed processing workflows.
• The ability to make architecture decisions across storage, processing, integration, governance, and security.
• Experience improving data platform performance, scalability, reliability, and operating costs.
• A track record of providing technical leadership, guiding engineering teams, and aligning architecture decisions with business objectives.

Preferred Credentials

• AWS Certified Solutions Architect – Professional or an equivalent advanced AWS certification.
• AWS data-related certification or demonstrable equivalent expertise.
• Experience with enterprise data governance frameworks and modern cloud analytics architectures.

Skills: aws glue,amazon emr,aws,amazon s3,data engineering,apache airflow,enterprise data,amazon redshift