WHAT DOES A DATA ARCHITECT DO?

Published: October 2, 2024 – The Data Architect delivers the Business Information Model (BIM) based on the TMF-SID, creates and updates key data entities to support solution architects in their data designs. This role involves designing data models, ensuring compliance with Data Management and Governance standards, and collaborating with enterprise data architects to integrate lower-level data models into the BIM. Additionally, the architect tracks remediation plans from Trusted Source Assessments and produces KPIs for the Data Programme to facilitate effective delivery.

A Review of Professional Skills and Functions for Data Architect

1. Data Architect Overview

  • Data Product Definition: Definition of data-related products and services, as well as proof-of-concept architectures and technical specifications in collaboration with internal customers and product managers.
  • MVP Feature Selection: Selection of features and technologies for MVP products and management of the backlog and delivery of data products with the product manager.
  • Data Transformation Collaboration: Collaborate with data analysts and domain experts to identify data transformation methods, tools, and technologies to build product offerings.
  • Data and AI Support: Support Data and AI adoption across the organization with data stewards.
  • Data Strategy Implementation: Implement data strategy in line with business processes and data products.
  • New Data Services Identification: Support product managers in identifying new data service needs and opportunities.
  • Data Management Design: Identify data sources (internal and external) and design data management plans aligned with the data strategy and product managers.
  • Data System Management: Design, implement, and manage data warehouses, data lakes, and data analytics systems with consumers and third parties.
  • Operational Coordination: Coordinate and collaborate with functional teams, stakeholders, and partners for smooth operation around data services.
  • Data Governance Collaboration: Collaborate with the data governance team as well as the Schneider Electric Digital EcoStruxure data architecture team on technical and business aspects with datasets.
  • Personal Development: Maintain personal and professional development through regular and focused activity, which covers the complete development spectrum (i.e., conferences, courses, personal coaching, etc.).
  • Discipline Advancement: Keep up to date personally with the latest thinking in a chosen subject/discipline by attending and presenting at conferences, producing research material for leading-edge journals, etc.
  • Accreditation Achievement: Achieve the required level of accreditation in the Group’s mandated Methodology and Tools.
  • Team Development Commitment: Demonstrate total commitment to the personal and professional development of all members of TTG.

2. Data Architect Job Description

  • Target Architecture Support: Support the development of the Target Architecture Model for Treasury encompassing process and data modeling for Big Data implementations, as well as conceptual, logical, and physical modeling.
  • Database Reverse Engineering: Proficient in reverse engineering databases and organizing logical data model presentations to be meaningful for analysis – Taxonomy/Hierarchy/Glossary.
  • Collaborative Architecture Development: Collaborate with other modelers on the Treasury Data Service team to develop the target Process and Data Architectures and integrate them with the corresponding Treasury data and technology architectures.
  • Architecture Roadmap Contribution: Work closely with technology project and architecture teams to develop and contribute to a comprehensive and cogent architecture roadmap.
  • Stakeholder Issue Analysis: Work with business/IT stakeholders to analyze issues and propose solutions. Understand process impacts in the context of cross-Treasury and wider processes.
  • Dependency Articulation: Be able to articulate dependencies and impacts on data and technology.
  • Regulatory Awareness: Incorporate awareness of the regulatory environment and impacts into proposed solutions.
  • Strategic Engagement: Engage in strategic discussions with Business Architects, ensuring process redesign displays full alignment with Treasury objectives.
  • Complex Situation Analysis: Use a range of techniques to analyze complex situations or issues and propose best-in-class solutions.
  • Quality Assurance Responsibility: Take responsibility for quality assurance for process and data architecture outputs.
  • Data Governance Model Development: Support the development of the Data Governance Model for Treasury in line with Chief Data Office policy and guidance.
  • Data Governance Integration: Work with the Treasury IT development team to ensure Data Governance principles are incorporated into design and implementation decisions.
  • Data Lineage Documentation: Work with the central Data Lineage technology team to establish current state data lineage documentation for Treasury systems and maintain lineage going forward.
  • Data Quality Analysis: Work with the central Data Quality (DQ) technology team to produce daily DQ analysis for key Treasury data sets using a selected DQ vendor toolkit.

3. Data Architect Functions

  • Data Migration Architecture: Develop architecture, policies, and standards for all data migration matters related to Oracle Fusion, Oracle CPQ, ServiceMax, and other applications.
  • Data Solutions Support: Develop and support data solutions across multiple data sources and targets.
  • Technical Strategy Leadership: Lead and modify technical strategies, handle problem resolutions and document issues.
  • Requirements Analysis: Perform requirement analysis, build architectural models, and estimate development and processing efforts for the solutions provided.
  • Data Tool Configuration: Configure and administer Data Tool Components (Informatica, Azure Data Pipeline, etc.).
  • ETL Standards Leadership: Take the leading role in implementing and maintaining ETL coding and development standards in an international team of developers.
  • Architecture Consultation: Provide architecture and design consultation for out-of-the-box solutions.
  • Quality Standards Assurance: Ensure industry quality standards and compliance requirements are achieved in pre-defined metrics.
  • User Mentorship: Mentor and train users to perform data duties.
  • Technology Research: Research new data technologies and technology trends.
  • Data Structure Modeling: Model and design the application data structure, storage, and integration.
  • Cost-Effective Solutions: Provide secure, stable, scalable, and cost-effective solutions to facilitate storage, integration, usage, access, and delivery of data assets across the business.
  • Statistical Analysis: Compare and analyze provided statistical information to identify patterns, relationships, and problems.
  • Database Design Fulfillment: Ensure that the database designs fulfill the requirements, including data volume and frequency needs.

4. Data Architect Accountabilities

  • Collaboration for Data Solutions: Collaborate and partner with business domain leads, data scientists, product owners, enterprise architects, and other functional leads to deliver world-class data solutions.
  • Data Processing Architecture: Architect data processing frameworks for ingestion and data quality (DQ).
  • Team Collaboration: Work with peers and technical personnel members of the Data Analytics Office.
  • Data Assembly: Assemble data from various sources into datasets to analyze the information to solve technical business problems and improve business efficiency.
  • Data Migration Strategy: Define data migration strategy and roadmap for core insurance systems leveraging knowledge of data pipelines.
  • ETL and Domain Knowledge: ETL and insurance domain knowledge.
  • Product Evaluation Participation: Participate in product evaluation for the semantic layer.
  • Migration Solution Development: Design and develop migration solutions to migrate data and other objects to AWS.
  • Business Value Articulation: Envision realizing and articulating the business value delivered by enterprise-wide data programs to key client business stakeholders.
  • Business Case Creation: Create and present business cases for data-related projects.
  • On-Premise to AWS Migration: Migration of on-premise EDW application to AWS EMR using Spark, Python, Hive, and Scala.
  • Technical Leadership: Provide technical leadership and direction to other data engineers and software engineers within the organization.
  • Technology Research and Influence: Research, recommend, and influence new data technologies and best practices within the organization.
  • Agile Collaboration: Work within an Agile environment, collaborating with multiple Agile teams.

5. Data Architect Job Summary

  • Business Information Model Delivery: Deliver business information model (BIM) based on the TMF-SID (Telecommunications Management Forum - Solution and Information Description), which will be used by solution architects as a basis for their data designs.
  • Data Subject Area Leadership: Lead for relevant data subject areas, create, improve, and update the BT-wide Business Information Model (BIM) covering key data entities.
  • Data Catalog Support: Support in building a data catalog to make data visible, understandable, and more usable; simplify data flows to improve data timeliness and integration, and reduce time to market.
  • Data Model Design: Design data models and collaborate with enterprise data architects to deliver the requirements.
  • Data Mapping and Integration Support: Support the mapping and integration of lower-level data and solution-specific logical models to the BIM.
  • Data Model Variation Resolution: Resolve variations in the data model, escalating to relevant Design Authorities where necessary.
  • Compliance Assurance: Responsible for ensuring compliance with Data Management and Data Governance standards.
  • Remediation Plan Tracking: Track remediation plan delivery as an outcome of Trusted Source Assessments of Authoritative Data Sources (ADS), identifying any resulting risks and issues.
  • KPI Production for Data Architecture: Produce Data Programme KPIs for Data Architecture to support Data Programme delivery.
  • Evidence Collection and Certification: Perform evidence checks, collect relevant approvals, and generate ADS certification submissions for the Data Architecture Council.
  • Qlik Sense ADS Maintenance: Support the maintenance of the Qlik Sense ADS reporting suite, collaborating with the Qlik Sense development team for any issues and resolving stakeholders’ queries.
  • MI and Compliance Reporting: Produce MI and report on ADS GDMS compliance status and Data Architecture Roadmap.
  • Audit Support: Support the Data Architecture Lead in audit/regulator-related activities on ADS, ensuring documentation is provided promptly.
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