Enterprise Data Architect; Position

Job Category: Engineering

Requisition Number: ENTER
001698

Location: Loftware UK, Winnersh, RED RG41 5TS
, G

Employment Type: Full-Time

Work Arrangement: Hybrid

Travel Required: No

Position Summary: The Enterprise Data Architect is responsible for designing, implementing, and maintaining the overall data architecture of the organization. This role involves creating a comprehensive data strategy to support the business's strategic goals, ensuring data consistency, integrity, and availability across various systems. The ideal candidate will have extensive experience in data architecture, data modeling, and data management, with a strong understanding of business intelligence (BI), data analytics, Lakehouse architecture, and technology.

• Discovery and Assessment
  - Understand what data exists and how it behaves.
• Migration Strategy & Planning
  - Define how migration will happen.
• Data Mapping and Transformation Design
  - Translate source data into target structures.
• Data Cleansing & Enrichment
  - Fix data before moving it.
• Migration Architecture & Pipeline Design
  - Design the technical movement of data.
• Data Migration Development & Testing
  - Build and validate pipelines.
• Data Reconciliation & Validation
  - Ensure migrated data is correct.
• Cutover Execution
  - Move into production.

• Develop and execute the enterprise data architecture strategy aligned with the organization’s goals.
• Collaborate with business leaders to understand data needs and ensure the architecture supports business objectives.
• Evaluate and recommend data management tools and technologies that align with the organization’s strategic vision.
• Implement master data management, reference data management, metadata management strategies to ensure data consistency, quality and security.

• Develop and implement data governance policies and standards, as well as performance indicators and quality metrics, to manage data effectively and ensure compliance with data-related policies and standards.
• Monitor data quality and performance metrics, addressing issues as they arise to maintain data integrity.

• Design and implement data models, data flows, and data integration strategies to support business processes.
• Develop and maintain comprehensive data architecture documentation, including data models, data dictionaries, and metadata.
• Establish data governance frameworks and best practices to ensure data quality, consistency, and security.

• Design and implement Lakehouse architectures that combine the features of data lakes and data warehouses, optimizing for both structured and unstructured data.
• Utilize Lakehouse platforms and tools to integrate, store, and analyze large volumes of data efficiently.
• Evaluate and recommend Lakehouse solutions and technologies, including Delta Lake, Apache Hudi, MS Fabric, Databricks, or Apache Iceberg, to enhance data processing and analytics.

• Design and implement BI architecture to support reporting, analytics, and decision-making processes.
• Develop and maintain BI data models, dashboards, and reports that provide actionable insights to business stakeholders.
• Evaluate and recommend BI tools and technologies to enhance data visualization and analysis capabilities.

• Lead cross-functional teams to drive data-related projects and initiatives.
• Communicate data architecture strategies and solutions to stakeholders at all levels, including executives.
• Mentor and provide guidance to junior data architects and data management staff.

Must-have

• Advanced SQL + data modeling
• Cloud data platform expertise
• ETL/ELT and pipeline design
• Data governance & security

Strong differentiators

• Real-time/event-driven architecture
• Data Ops / automation
• Data mesh / modern architecture patterns
• AI/ML data infrastructure and application
• Data observability platforms

• Conceptual, logical, and physical data modeling
• Dimensional modeling (star/snowflake schemas)
• Normalization vs. denormalization tradeoffs
• Data vault modeling (increasingly important in modern architectures)
• Master Data Management (MDM) concepts

• ER/Studio, ERwin, Lucidchart, SQL DB tools

• Deep expertise in at least one major cloud:
  • Azure (Synapse, Data Factory, Fabric)
  • AWS (Redshift, Glue, Lake Formation)
  • Google Cloud (Big Query, Dataflow)
• Understanding of:
  • Data lakes vs. lake houses
  • Distributed storage (S3, ADLS)
  • Serverless vs provisioned architectures

• ETL / ELT design patterns
• Batch and real-time streaming architectures