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 to 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 :

1. AWS Glue

2. AWS Glue Data Catalog

3. Data Validation

4. Data Governance

5. AWS IAM

6. 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

Work Location :

- Remote