Senior Data Architect - AWS

Zain Group; Ernakulam; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

A leading tech company is in search of a Senior Data Architect to lead data and analytics initiatives focused on AWS cloud platforms. This strategic role involves architecting and governing enterprise-grade data solutions and advanced analytics platforms. The ideal candidate will have over 10 years of experience, particularly in cloud-based platforms, and hold multiple AWS certifications. You will mentor teams, design scalable architectures, and ensure compliance with data governance standards.

Qualifications

• 10+ years of experience in data architecture or related roles.
• 5 years specifically in cloud data platforms with AWS focus.
• Mandatory AWS certifications in Data Analytics and Machine Learning.

Responsibilities

• Define and execute the enterprise data architecture strategy.
• Own AWS data platform architecture and design scalable data ingestion pipelines.
• Build and mentor high-performing teams of data and ML engineers.

Skills

Cloud-based data platforms (AWS emphasis)

Data modeling (dimensional, data vault)

ETL/ELT pipeline building

Data governance and security frameworks

Machine Learning & MLOps

Education

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

Tools

AWS data services (S3, Glue, Redshift, Athena)

Apache Spark

SQL

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.

Qualifications

• 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.