Data Architecture & Modelling:
Lead the design and implementation of robust, scalable data warehouse solutions on the Azure platform, using the Kimball methodology to create efficient staging and production layers.
ETL/ELT Development:
Design, build, and maintain ETL processes to integrate data from diverse sources, including APIs. Utilise Azure Data Factory and other tools to ensure efficient and reliable data ingestion into Azure SQL Server.
Reporting & Analytics Support:
Build and manage insightful reports and dashboards using Power BI and SSRS. Collaborate with data analysts to support complex SQL queries and ensure seamless data delivery for business intelligence.
Performance & Optimization:
Analyse and optimize existing data workflows and queries for enhanced performance, scalability, and cost efficiency.
Data Governance & Quality:
Champion data quality, reliability, and integrity. Implement proactive data quality checks, monitoring processes, and data governance policies to maintain high standards.
Automation & Version Control:
Develop automated scripts using Python, PowerShell, or Bash for efficient data extraction and integration. Maintain and manage version control of all workflows and scripts using Git.
Collaboration & Communication:
Work closely with stakeholders to gather requirements, troubleshoot issues, and provide data-driven solutions. Effectively communicate complex technical concepts to both technical and non-technical audiences.
Documentation:
Create and maintain clear, comprehensive documentation for data models, processes, and workflows to support knowledge sharing.

What We re Looking For

Required:

Experience:
A minimum of 5 years of hands-on experience designing, building, and managing data warehouses, with at least 2 years of experience analysing and extracting data from diverse sources (including REST APIs).
SQL Mastery:
Demonstrated mastery of SQL, including the ability to write complex queries and create tables, views, stored procedures, and functions.
Azure Proficiency:
Expertise in the Azure data stack, including
Azure Data Factory
,
Azure SQL Server
,
Azure DevOps
,
Key Vaults
, and
Logic Apps
.
ETL Tools:
Proven experience building and maintaining ETL pipelines using tools such as
Azure Data Factory
and
SSIS
.
Reporting Tools:
Experience developing reports and dashboards with visualization tools like
Power BI
and
SSRS
.
Scripting:
Proficiency in scripting languages such as
Python
,
PowerShell
, or
Bash
for automation and orchestration.
Generative AI:
Knowledge of Generative AI principles and their practical application in data engineering.
Version Control:
Experience using source control tools, particularly
Git
.
Core Competencies:
Strong data modelling, problem-solving, and performance-tuning skills. Familiarity with Agile workflows and CI/CD pipelines.

Desirable:

Cloud Cost Management:
Understanding of strategies to manage and optimize Azure resource costs.
Data Quality Tooling:
Hands-on experience with tools and practices to monitor and improve data quality within a data pipeline.

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