Data and Analytics Architect

Data & Analytics

Responsibilities

• Define, design and take responsibility for end-to-end scalable data architecture from definition phase to go-live phase aligned to enterprise architecture and industry standards delivering data driven business value.
• Work closely with internal teams including compliance, privacy, risk, information security, platform support and operations to ensure all business requirements are considered in the design process.
• Participate in the development of architectures, strategies, and policies around data; including data governance, master data management, metadata management, data quality and data security.
• Provide technical leadership and be an advocate for the data capabilities according to Project Need.
• Define scalable and reusable standards and frameworks for the data area.
• Perform and lead data analysis activities including data profiling, data quality, data transformation rules and integration requirements. Develop, document and maintain conceptual, logical and physical data models that accurately reflect the existing environment and reuse these when applicable.
• Oversee the full data life cycle and provide guidance and direction to development teams - including reuse, design, documentation, validation and continuous improvement of the deliveries.
• Conduct analysis to provide actionable insights, identify trends, and measure performance.
• Visualise complex analytics output to enable and increase understanding of decision-makers
• Apply statistics and mathematics models to understand and solve business problems on data.
• Collaborate with data practitioners to implement and deploy scalable solutions.
• Accountable for the quality of analytics, analytical leadership of the product team, and delivery.
• Organize code and manipulate very large complex data sets for data exploration/mining/quality and business value purposes.
• Define standards and frameworks for data analysis.

Core Skills

• Data Architecture & Data Modeling
• Data Engineering & Data Integration (ETL/ELT)
• Data Warehousing & Data Lakehouse Design
• Data Governance, Data Quality & Master Data Management
• Data Analysis, Data Profiling & Data Transformation
• Business Intelligence & Data Visualization
• Statistical Analysis & Advanced Analytics
• Big Data Processing & Data Mining
• Cloud Data Platform Engineering
• Data Security, Privacy & Compliance
• SQL & Programming (Python/Spark)
• DataOps, CI/CD & Automation
• Stakeholder Management and Technical Leadership
• Atleast one solution implementation in Engineering use case

Common Tools & Technologies

Data Platforms & Cloud

• Microsoft Fabric
• Azure Synapse Analytics
• Azure Data Factory
• Azure Databricks
• Azure Data Lake Storage
• Snowflake

Data Engineering & Big Data

• Apache Spark
• Databricks
• Kafka
• Hadoop (where applicable)

Databases

• SQL Server
• Azure SQL Database