The Data Architect plays a critical role in designing and building foundational data platforms that enable customers to move, organize, and analyze their business data at scale. In this position, you will partner with client team members to architect, implement, and deploy data warehouse solutions across multiple cloud platforms and customer organizations. This position requires deep expertise in medallion layer design, ELT implementation, semantic layer architecture and BI tool integration. You should be able to lead architecture discussions, guide technical execution, and mentor junior engineers. As a senior technical contributor, you will take ownership of complex data warehouse solutions, conduct technical discovery with customers, and ensure scalable, maintainable data architectures. You will act as a trusted technical advisor to both internal teams and customer stakeholders while driving best practices in data design, pipeline development, semantic layer modelling, and BI implementation.

Responsibilities

• Design medallion layer data warehouse schemas (bronze, silver, gold) on Databricks, Redshift, Snowflake and/or BigQuery.
• Select and implement ELT tools to move data from source systems into the warehouse. You'll evaluate options, configure pipelines, and troubleshoot performance issues.
• Build and optimize semantic layers on top of the warehouse so analysts and business users can query data without writing SQL.
• Connect data warehouses to BI platforms (Tableau, Looker, Power BI, or similar) to enable self-service analytics and reporting.
• Translate business requirements into scalable technical integration architectures and implementation strategies.
• Facilitate discovery sessions and technical workshops with clients to understand system landscapes, integration requirements, and data flows.
• Own complex integration solutions from design through deployment, ensuring high performance, scalability, and maintainability.
• Advise on data governance, quality checks, and cost optimization as warehouses scale. 
• Drive complex technical design conversations with clients and recommend configuration and architectural solutions aligned with best practices.
• Collaborate with cross-functional teams including architects, consultants, and client technical teams to deliver integration solutions.
• Proactively flag project risks, technical limitations, or architectural concerns early and propose mitigation strategies.
• Stay current with emerging integration technologies, tools, and patterns and evaluate opportunities to apply them to client solutions.

Basic Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Management Information Systems, or a related field.
• 5+ years building data warehouses or data lakes on at least two cloud platforms (Databricks, Redshift, Snowflake, or BigQuery). 
• Hands on experience designing medallion or similar layered architectures (atomic, aggregated, and presentation layers).
• Shipped at least two end-to-end ELT implementations with thorough understanding of the landscape and trade-offs between products. 
• Design AI powered semantic models to accelerate data discovery and enable intelligent query generation for end users. 
• Build and optimize semantic layers on top of the warehouse so analysts and business users can query data without writing SQL.
• Connect data warehouses to BI platforms (Tableau, Looker, Power BI, or similar) to enable self-service analytics and reporting 
• Strong SQL knowledge, write efficient queries and understand query optimization.
• Excellent written and verbal communication skills with the ability to present technical concepts to both technical and non-technical stakeholders.
• Ability to manage multiple complex projects and priorities in a fast-paced consulting environment.
• Passion for building scalable integration solutions and ensuring data accuracy and reliability.

Nice to Have

• Experience with FiveTran, Airbyte or other ELT platforms.
• Familiarity with data governance tooling (metadata management, lineage tracking, data quality platforms).
• Experience with AI-assisted development tools like Claude Code to accelerate pipeline and configuration work.
• Background in financial services, SaaS, or private equity-backed companies.
• Ability to mentor junior data engineers or analysts.
• Experience with AI-powered analytics or semantic modeling capabilities in modern BI platforms.