WHAT DOES A LEAD DATA ARCHITECT DO?
Published: Jan 09, 2026 - The Lead Data Architect leads consulting teams to define and deliver data strategies, architectures, and implementations that enable advanced analytics, reporting, and data science capabilities. This role acts as a trusted advisor by shaping client solutions, supporting budgeting, proposals, and go-to-market offerings, and collaborating across technology practices to deliver complex engagements. The lead also drives practice growth through thought leadership, sales enablement, team modeling, and talent development.
A Review of Professional Skills and Functions for Lead Data Architect
1. Lead Data Architect Duties
- Requirements Analysis: Conduct a thorough requirements analysis to understand mission objectives and existing system attributes.
- Architecture Planning: Develop as-is and to-be architecture and plans.
- Technical Leadership: Lead and mentor team members in the implementation of the plan and contribute to the completion of technical objectives.
- Solution Delivery: Design, implement, schedule test and deploy full features and components of solutions.
- Quality Assurance: Ensure quality delivery of software through testing and reviews.
- Stakeholder Engagement: Regularly engage with stakeholders including Customer Prime and AnaVation and keep them informed of progress and plans.
- Data Governance: Facilitate data governance while educating staff on data-related policies and procedures to ensure accuracy and accessibility.
- Architecture Strategy: Report to the Director of Information Technology and lead the design and execution of data architecture strategies.
- Data Design: Design complex repositories aligned with data requirements analysis and metadata management.
2. Lead Data Architect Details
- BI Collaboration: Collaborate with business product and engineering partners to help define reporting and BI needs.
- Data Requirements: Work with product owners, technical analysts, end users and developers to define data requirements and data structures for reporting and BI solutions.
- Data Sourcing: Collaborate with internal and external stakeholders on data rationalization and data sourcing.
- Data Modeling: Lead the design and implementation of conceptual and logical models for operational data stores, data warehouses or data marts.
- Model Evolution: Evolve data models to meet new and changing business requirements and growth.
- Data Reusability: Foster data reusability through modeling consistency, simplicity and discoverability.
- BI Architecture: Lead the creation of robust, efficient and scalable reporting and BI solutions to meet requirements.
- Architecture Standards: Recommend standards, policies and approaches for development processes, technology and tooling.
- Quality Assurance: Guide industry best practices and ensure the quality of deliverables.
- ETL Development: Lead the design and development of data ingestion and ETL processes using approved modern tools.
- Solution Alignment: Work with solution and technical architects to ensure solutions meet functional and non-functional requirements including security, performance, resilience and maintainability.
- Architecture Communication: Communicate data architecture to stakeholders and technologists.
- Data Governance: Participate in and advise on solution reviews, data quality management and data governance reviews.
- Technology Evaluation: Monitor and recommend emerging data technologies.
- Tool Procurement: Assist in data tools and services procurement.
3. Lead Data Architect Responsibilities
- Design Governance: Own and create Conceptual Design statement documents to support the Group IT Design Authority Governance process, based on material change programmes of work and Design Artefacts
- Capability Modeling: Analysis, creation and updating of Capability Models, Business and IT for the area of architectural concern
- Architecture Documentation: Create and update As-Is Architecture Documents
- Reference Architecture: Create and update Reference Architectures and act as the champion of RA through approval governance
- Target Architecture: Create and update the To Be Reference Architecture and act as the champion of RA through approval governance
- Solution Review: Review and contribute to HLD / Architecture Specification Documents for specific projects
- Stakeholder Engagement: Engage with senior leadership within JMIT and JM Business to facilitate and lead Architecture discussions and decisions
- Data Evaluation: Participate in due diligence of new software purchases by reviewing all proposed data models contained in packaged or commercially available applications
- Data Integration: Participate in all data integration and enterprise information management programs and projects to rationalise data processing
4. Lead Data Architect Accountabilities
- Requirements Analysis: Gather, analyze and document business and functional data product and system requirements
- Solution Design: Translate the needs of the business into applicable system design solutions via prototyping and configuration
- Strategy Alignment: Set and influence cross-divisional strategies and business processes
- Data Architecture: Understand the strategic data needs of the business and translate them into data system architecture and design
- Business Partnership: Partner with internal and external departments to improve processes, data detail quality and business applications
- Effort Estimation: Assist in providing estimates of task effort and required resource skills
- Application Development: Build, maintain and enhance business applications and solutions with a focus on maintaining the integrity of business applications, data and systems
- Technical Leadership: Serve as technical and team lead for system design and best practices
- Subject Expertise: Serve as an internal resource and subject matter expert to colleagues and support the resolution of complex problems and issues related to data used for reporting and analytics purposes
- Data Mapping: Assist in the definition, documentation and development of mapping rules for moving data from disparate data sources into various components and subject areas of the BI environment
- Architectural Oversight: Move between high-level architectural review/design and the "roll up the sleeves" level of providing architectural oversight for project delivery
5. Lead Data Architect Functions
- Delivery Leadership: Lead consulting delivery teams across all aspects of data strategy, data architecture, and implementation projects aimed at enabling new reporting, visualization, Big Data, and data science capabilities
- Architecture Strategy: Define data architecture and strategies based on client problem statements
- Budget Advisory: Provide rationale and justifications to support client budgeting processes
- Client Trust: Earn the trust of clients and position Entech as a trusted advisor on all data-related topics
- Practice Collaboration: Collaborate with other technology practice leads to ensure Entech delivers the firm’s full capabilities when supporting large and complex technology projects
- Technology Awareness: Stay current on leading data technologies, services, and solutions in the marketplace
- Thought Leadership: Author thought leadership content including blogs, whitepapers, and social media on data topics to strengthen Entech’s competitive positioning
- Sales Support: Support sales and account management teams in triaging inbound project opportunities and defining proposed solution support
- Delivery Planning: Develop implementation team models, duration estimates, and cost models to ensure margin and delivery quality goals are met
- Proposal Development: Aid in the preparation of proposals, SOWs, and RFP or RFI responses for Entech data offerings
- Offering Definition: Define Entech data offerings and go-to-market positioning
- Sales Enablement: Educate sales and account management teams on data topics, trends, and common client scenarios where Entech services deliver value
- Case Curation: Aggregate and curate case studies for all data-related delivery projects
- Practice Growth: Define a data practice growth plan, define roles and job descriptions, and participate in interviewing and hiring data practitioners
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