Data Architect

Role & responsibilities

• Design, develop, and maintain scalable enterprise data architecture incorporating data warehouse, data lake, and data mesh concepts
• Create and maintain data models, schemas, and mappings that support Reporting, business intelligence, analytics, and AI/ML initiatives
• Establish data integration patterns for batch and real-time processing using AWS services (Glue, DMS, Lambda),

  Redshift, Snowflake or Data Bricks

  [KM1] .
• Define technical specifications for data storage, data processing, and data access patterns

• Develop data models and enforce data architecture standards

  [KM2] , policies, and best practices
• Partner with business stakeholders to translate requirements into architectural solutions
• Lead data modernization initiatives, including legacy system migrations
• Create roadmaps for evolving data architecture to support future business needs
• Provide expert guidance on complex data problems and architectural decisions

Preferred candidate profile

• Bachelors degree in computer science, Information Systems, or related field; Masters degree preferred
• 8+ years of experience in

  data architecture, database design, data modelling or related roles[KM1]

• 5+ years of experience with cloud data platforms, particularly AWS data services
• 3+ years of experience architecting MPP database solutions (Redshift, Snowflake, etc.)
• Expert knowledge of data warehouse architecture and dimensional modelling
• Strong understanding of

  AWS

  [KM2] data services ecosystem (Redshift, S3, Glue, DMS, Lambda)
• Experience with SQL Server and migration to cloud data platforms
• Proficiency in data modelling, entity relationship diagrams, and schema design
• Working knowledge of data integration patterns and technologies (ETL/ELT, CDC)
• Experience with one or more programming/scripting languages (Python, SQL, Shell)
• Familiarity with data lake architectures and technologies (Parquet, Delta Lake, Athena)
• Excellent verbal and written communication skills, with ability to translate complex technical concepts to varied audiences
• Strong stakeholder management and influencing skills
• Experience implementing data warehouse, data lake and data mesh architectures
• Good to have knowledge of machine learning workflows and feature engineering

• Understanding of regulatory requirements related to data (Fed Ramp, GDPR, CCPA, etc.)[KM3]

• Experience with

  big data technologies

  [KM4] (Spark, Hadoop)