AWS Data Architect

• Design and implement end-to-end data architectures, including data ingestion, transformation, storage, and analytics solutions.
• Define enterprise data warehouse architectures and develop scalable, high-performance data processing frameworks.
• Build and optimize data pipelines using Python, PySpark, SQL, and dbt.
• Design, develop, and manage cloud-based data solutions using AWS services, particularly Amazon Redshift and AWS Glue.
• Establish data modeling standards, data structures, transformation strategies, and architectural best practices.
• Develop distributed data processing solutions using Apache Spark and related big data technologies.
• Evaluate and implement Databricks solutions to support advanced data engineering and analytics requirements.
• Leverage Docker and Kubernetes to support containerized workloads and scalable data applications.
• Implement Infrastructure as Code using Terraform to automate cloud infrastructure provisioning and management.
• Establish and maintain CI/CD pipelines using Jenkins and other automation tools.
• Work with technologies such as Hive to support data processing, integration, and analytics workloads.
• Ensure data quality, reliability, scalability, security, and performance across enterprise data platforms.
• Apply Agile development methodologies and Test-Driven Development (TDD) practices to improve software quality and delivery.
• Identify performance bottlenecks, troubleshoot complex data engineering issues, and implement optimization strategies.
• Collaborate with engineering teams, data scientists, business stakeholders, and technical leadership to align data architecture with business objectives.
• Provide technical leadership, review architectural designs, and guide engineering teams in implementing scalable data solutions.

We are seeking an experienced AWS Data Architect to lead the design and development of scalable, secure, and high-performance data solutions on AWS. The ideal candidate will have extensive experience in data engineering, enterprise data architecture, data warehouse design, and cloud-based data platforms.

The successful candidate will bring strong hands-on expertise in Python, PySpark, SQL, dbt, Amazon Redshift, and AWS Glue, along with a solid understanding of data modeling, distributed processing, and software architecture. This role requires the ability to define end-to-end data architecture, optimize data pipelines, establish engineering best practices, and collaborate with cross-functional teams to deliver reliable, enterprise-grade data solutions., * 15-20 years of experience in software engineering, data engineering, machine learning, or related technical fields.

• Strong experience designing end-to-end data solutions and enterprise data warehouse architectures.
• Hands-on expertise in Python, PySpark, SQL, and dbt.
• Extensive experience with AWS cloud services, particularly Amazon Redshift and AWS Glue.
• Strong understanding of data structures, data modeling, database design, and software architecture.
• Experience designing and optimizing large-scale data pipelines and distributed processing solutions.
• Knowledge of Apache Spark and big data technologies such as Hive.
• Experience with Docker, Kubernetes, Jenkins, and Terraform is highly desirable.
• Strong preference for candidates with hands-on Databricks experience.
• Understanding of cloud infrastructure automation, CI/CD pipelines, and DevOps practices.
• Experience working in Agile development environments and applying Test-Driven Development principles.
• Strong analytical, problem-solving, and technical decision-making skills.
• Excellent written and verbal communication skills, with the ability to collaborate across technical and business teams.
• Bachelor’s degree in Computer Science or a related field, or equivalent professional experience.