Senior Data Architect - AWS

ZainTECH; Ernakulam; On-site; INR 2,000,000 - 2,500,000; Full time

Job summary

A tech solutions company is seeking a Senior Data Architect - AWS to lead data initiatives focused on AWS cloud platforms. This pivotal role involves architecting enterprise-grade data solutions, implementing advanced analytics, and governing machine learning infrastructures. The ideal candidate will have over 10 years of experience, with specific proficiency in cloud data architecture, AWS services, and team leadership. This position promises to enhance business value through data insights and intelligent automation.

Qualifications

• 10+ years of experience in data architecture, data engineering, or related roles.
• At least 5 years specifically in cloud-based data platforms (AWS emphasis).
• AWS Certified Data Analytics Specialty, AWS Certified Machine Learning Specialty, and Azure Data Engineer Associate (DP-203) are mandatory certifications.

Responsibilities

• Define and execute the enterprise data architecture strategy.
• Own AWS data platform architecture and design scalable data ingestion pipelines.
• Design and implement end-to-end ML platforms and MLOps frameworks.

Skills

AWS data services (S3, Glue, Redshift)

ETL/ELT pipelines using Python, PySpark, SQL

Data modeling (dimensional, data vault)

Distributed data processing frameworks (Apache Spark, Kafka, Flink)

Education

Bachelor's or Master's degree in a quantitative discipline

Tools

AWS Glue

Amazon SageMaker

Job description

The Senior Data Architect - AWS leads data and analytics initiatives, with a primary focus on AWS cloud platforms. This strategic role is responsible for architecting, implementing, and governing enterprise-grade data solutions, advanced analytics platforms, and machine learning infrastructure. The ideal candidate serves as a data thought leader, designing scalable, secure, and performant architectures that drive business value through insights and intelligent automation.

Responsibilities

• Data Architecture & Strategy: Define and execute the enterprise data architecture strategy, establishing architectural standards and best practices for data lakes, warehouses, and streaming analytics. Lead data modernization roadmaps for transitioning legacy systems to cloud-native AWS architectures.
• Platform Leadership: Own AWS data platform architecture, including Lakehouse and Data Mesh patterns, metadata management, and data lineage. Design scalable data ingestion pipelines and processing frameworks using tools like Glue, EMR, and Step Functions.
• Machine Learning & MLOps: Design and implement end-to-end ML platforms and MLOps frameworks using Amazon SageMaker. Architect scalable inference solutions and automate ML pipelines to enable reproducible, version-controlled workflows.
• Cross-Platform Integration: Architect hybrid and multi-cloud solutions connecting AWS with Azure and on-premises systems. Develop unified data access patterns and federated query capabilities.
• Governance & Security: Design comprehensive data governance and security frameworks, including encryption, fine-grained access control, and automated data quality validation pipelines. Ensure compliance with regulatory requirements (GDPR, CCPA).
• Team Leadership: Build and mentor high-performing teams of data and ML engineers. Conduct architecture reviews and collaborate with data scientists and business stakeholders to translate analytical requirements into robust technical solutions.

Requirements

• 10+ years of experience in data architecture, data engineering, or related roles, with at least 5 years specifically in cloud-based data platforms (AWS emphasis).
• Bachelor's or Master's degree in a quantitative discipline (Computer Science, Data Science, Engineering, etc.) is mandatory.
• Mandatory Certifications: AWS Certified Data Analytics Specialty, AWS Certified Machine Learning Specialty, and Azure Data Engineer Associate (DP-203).
• Deep technical expertise in AWS data services (e.g., S3, Glue, Redshift, Athena, EMR, Kinesis, SageMaker) and database technologies.
• Strong expertise in data modeling (dimensional, data vault) and distributed data processing frameworks (Apache Spark, Kafka, Flink).
• Proven hands-on experience building ETL/ELT pipelines using Python, PySpark, and SQL.