Senior Data Architect

Data Engineer designs, builds, and maintains scalable data solutions to enable advanced analytics and business intelligence across ***’s enterprise.

What will this person be working on

• Design and implement ETL pipelines using AWS services (Glue, EMR, DMS, S3, Redshift).

• Orchestrate workflows with AWS Step Functions, EventBridge, and Lambda.

• Integrate CI/CD pipelines with GitHub and AWS CDK for automated deployments.

• Develop conceptual, logical, and physical data models for operational and analytical systems.

• Optimize queries, normalize datasets, and apply performance tuning techniques.

• Use Python, PySpark, and SQL for data transformation and automation.

• Monitor pipeline performance using CloudWatch and Glue job logs.

• Troubleshoot and resolve data quality and performance issues proactively.

Minimum Experience

• 8–10 years in Data Engineering or related roles.

• Proven track record in AWS-based data solutions and orchestration

• Integration with ERP systems (SAP, Homegrown ERP Systems)

• API-based Data Exchange between Manufacturing, Supply Chain legacy applications and AWS pipelines

• Metadata Management for compliance attributes

• Audit Trails & Reporting for compliance verification

• Expertise in cloud to design, build, and maintain data-driven solutions

• Skilled in Data Architecture and Data Engineering with a strong background in Supply Chain domain

• Experienced in Data Modeling (Conceptual, Logical, and Physical), ETL optimizations, Query optimizations and Performance tuning

Technical Skills

• Languages: Python, PySpark, SQL

• AWS Services: Glue, EMR, EC2, Lambda, DMS, S3, Redshift, RDS

• Data Governance: Informatica CDGC/CDQ

• DevOps Tools: Git, GitHub, AWS CDK

• Security: IAM, encryption policies

• Monitoring: CloudWatch, Glue Catalog, Athena

• Strong integration background with DB2, UDB, SQL Server etc

Soft Skills:

• Strong communication and collaboration skills.

• Ability to work with cross-functional teams and stakeholders.