Technical Specialist

Job Summary

Job Title

Senior Data Analyst / Data Architect

Experience

10+ Years

Location: UK/ Flexible / Hybrid

Role Summary

We are seeking an experienced Senior Data Analyst / Data Architect to lead the design, development, and optimization of enterprise-scale data platforms and analytical solutions. The ideal candidate will possess strong expertise in data modeling, cloud data warehousing, business analytics, and modern data engineering practices. This role requires close collaboration with business stakeholders, architects, analysts, and engineering teams to transform complex business requirements into scalable and performant analytical solutions.

Key Responsibilities

Key Responsibilities

Data Architecture & Design

• Design and implement enterprise-grade data architectures supporting analytics, reporting, and AI/ML initiatives.
• Develop conceptual, logical, and physical data models aligned with business requirements.
• Establish scalable dimensional, semantic, and canonical data models for enterprise reporting.
• Define data standards, governance principles, and best practices across the data ecosystem.
• Lead architecture reviews and ensure adherence to enterprise architecture standards.

Analytics Engineering

• Build and maintain scalable, reusable, and modular data models using dbt and Snowflake.
• Transform legacy data assets and complex database views into optimized cloud-native analytical structures.
• Develop curated semantic layers to enable self-service analytics and business intelligence.
• Implement automation for data transformations, testing, deployment, and monitoring.

Data Analysis & Business Insights

• Partner with business stakeholders to understand analytical requirements and define KPIs.
• Translate business problems into data solutions and actionable insights.
• Perform advanced analysis of large datasets and provide strategic recommendations.
• Support executive dashboards, operational reporting, and performance measurement frameworks.
• Drive data-driven decision making through high-quality analytical outputs.

Data Quality & Governance

• Define and implement data quality frameworks, validation rules, and monitoring processes.
• Establish metadata management, data cataloging, and lineage practices.
• Ensure compliance with data governance, security, privacy, and regulatory standards.
• Develop policies for master data management and data stewardship.

Performance Optimization

• Optimize Snowflake storage, compute resources, and warehouse configurations.
• Improve query performance, data loading processes, and reporting response times.
• Monitor platform utilization and recommend cost optimization strategies.
• Implement access controls, role-based security, and attribute-based access control (ABAC).

Leadership & Collaboration

• Mentor junior analysts, engineers, and data modelers.
• Lead technical workshops, design discussions, and architecture reviews.
• Collaborate with product owners, business users, data scientists, and engineering teams.
• Act as a trusted advisor for data strategy and modernization initiatives.

Skill Requirements

Technical Requirements

Core Platforms

• Expert-level experience with Snowflake Cloud Data Platform.
• Strong hands-on expertise in dbt Core and dbt Cloud.
• Experience with modern cloud platforms such as AWS, Azure, or GCP.
• Familiarity with data orchestration tools such as Airflow, Azure Data Factory, or AWS Glue.

Data Modeling Expertise

• Dimensional Modeling (Kimball Methodology).
• Semantic Layer Design and Business Metrics Modeling.
• Data Vault and Enterprise Data Warehouse concepts.
• Logical, Physical, and Canonical Data Modeling.
• Star Schema and Snowflake Schema design.

Analytics & BI

• Power BI, Tableau, Looker, or equivalent BI platforms.
• KPI framework development and executive dashboard design.
• Advanced SQL and analytical problem-solving.
• Business requirements gathering and stakeholder management.

Data Engineering

• ETL/ELT design and implementation.
• Data pipeline development and optimization.
• Data quality validation and automated testing.
• Batch and near real-time data processing concepts.

Software Engineering Practices

• Git-based source control and branching strategies.
• CI/CD pipeline implementation.
• Automated testing frameworks.
• Agile and DevOps methodologies.
• Infrastructure-as-Code knowledge is a plus.

Other Requirements