Position Overview
We are seeking an experienced SageMaker Unified Studio (SUS) Professional with strong
expertise in building, validating, and governing enterprise data and AI platforms on AWS.
The ideal candidate should have hands-on experience implementing Amazon SageMaker
Unified Studio solutions and supporting modern data engineering and AI/ML workflows.
The candidate will work closely with cross-functional teams to design, implement, validate,
and manage scalable cloud-native data platforms while ensuring governance, security, and
compliance across the data lifecycle.
Key Responsibilities
Design, implement, and support solutions using Amazon SageMaker Unified Studio
(SUS).
Build and maintain enterprise data platforms leveraging AWS native services.
Configure and manage AWS Glue, Data Catalog, and data ingestion pipelines.
Implement data governance, metadata management, and access controls using AWS
governance services.
Develop and manage analytical and AI/ML workflows using Jupyter Notebooks
within SageMaker Unified Studio.
Perform solution validation, testing, and deployment of data platform components.
Collaborate with data engineers, data scientists, architects, and business stakeholders
to deliver scalable cloud solutions.
Support data cataloging, lineage, governance, and security best practices.
Troubleshoot platform issues and optimize performance across AWS services.
Participate in architecture discussions and contribute to platform modernization
initiatives.
Required Skills & Experience
7–12+ years of experience in Data Engineering, Cloud Data Platforms, or AI/ML
Platform Engineering.
Strong hands-on experience with Amazon SageMaker Unified Studio (SUS).
Experience delivering end-to-end SageMaker Unified Studio implementations or
related AWS AI/ML platform projects.
Strong understanding of platform validation and implementation best practices.
Hands-on experience with:
o AWS Glue
o AWS Glue Data Catalog
o Data Validation
o Data Governance
o AWS IAM
o Amazon S3
Experience creating and managing Jupyter Notebooks for data engineering,
analytics, and machine learning workloads.
Strong knowledge of metadata management, cataloging, and governance frameworks.
Experience working with enterprise-scale cloud data platforms.
Proficiency in Python and SQL.
Knowledge of ETL/ELT processes and modern data architectures.
Familiarity with AWS security, access management, and compliance standards.
Preferred Skills
Experience with AWS Lake Formation.
Exposure to Amazon SageMaker Pipelines and MLOps practices.
Experience with DataOps and CI/CD implementations.
Knowledge of Apache Spark or Databricks.
Understanding of Data Lakehouse architectures.
Experience working with Infrastructure as Code (Terraform or CloudFormation).
Preferred Domain Experience
Candidates with experience in Life Sciences, Pharmaceuticals, Biotechnology, or
Healthcare environments will be preferred.
Ideal Candidate Profile
Proven experience implementing and supporting Amazon SageMaker Unified
Studio environments.
Strong expertise in AWS Glue, Data Catalog, Governance, and Validation.
Hands-on experience using Jupyter Notebooks for analytics and AI/ML
development.
Strong analytical and problem-solving abilities.
Excellent communication and stakeholder management skills.
Ability to work independently in enterprise cloud environments.
Primary Skills
Amazon SageMaker Unified Studio (SUS)
AWS Glue
AWS Glue Data Catalog
Data Governance
Data Validation
Jupyter Notebooks
AWS IAM
Amazon S3
Python
SQL
Pay: ₹800,000.00 - ₹2,700,000.00 per year
Work Location: Remote
