Enterprise Data Architect (AWS)

Enterprise Data Architect (AWS)

Remote

FTE

Key Responsibilities:

• Conduct thorough analyses of existing data architectures across multiple domains, identifying fragmentation, redundancies, and gaps impacting efficiency, quality, and integration. This includes evaluating the suitability of existing data assets for AI/ML applications and identifying data gaps.

• Provide architectural guidance, strategic vision, and actionable roadmaps transitioning fragmented states into cohesive enterprise-level data architectures aligned with EDM objectives. This includes defining a data architecture that supports the ingestion, processing, and storage of data required for AI initiatives across the organization.

• Develop clear standards, principles, guidelines, and best practices for data management, integration, security, governance, and data quality.

• Engage directly in data modeling, schema design, database development, and integration activities, demonstrating practical expertise in relational databases, NoSQL, data lakes, and cloud-native databases. Ensure data models are designed to support both traditional reporting and advanced analytical needs, including AI/ML model training and inference.

• Use industry-leading data modeling tools (e.g., ERwin, ER/Studio, Sparx EA, SAP PowerDesigner) to develop conceptual, logical, and physical data models, effectively translating business requirements into structured data solutions.

• Leverage AWS cloud services and technologies (e.g., AWS Redshift, RDS, S3, Glue, Lambda, Athena, SageMaker) to provide consultancies in designing, developing, and implementing scalable and reliable cloud-based data architectures

• Collaborate extensively with EDM leadership (Data Governance, Data Quality, Master Data Management) and business/domain stakeholders to translate business requirements into robust and scalable architectural solutions.

• Provide mentorship and hands-on guidance to IT solution teams, ensuring adherence to data architecture best practices during development phases through structured reviews and governance processes. Train teams on data architecture best practices for supporting AI/ML development and deployment.

Required Characteristics:

• Hands-on experience with database technologies (relational databases such as Oracle, SQL Server, PostgreSQL, and/or NoSQL databases such as MongoDB, Cassandra).

• Practical experience in designing and implementing cloud data architectures specifically within AWS (e.g., AWS Redshift, RDS, S3, Glue, Lambda, Athena, SageMaker).

• Extensive experience using data modeling tools (such as ERwin, ER/Studio, Sparx Enterprise Architect, SAP PowerDesigner) to create conceptual, logical, and physical data models.

• Deep understanding of data lifecycle management, data integration, governance principles, and semantic consistency across heterogeneous environments.

• Proven ability to identify and address data architecture pitfalls proactively through structured review processes