Senior Data Architect

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Responsibilities:

• Develop and evolve the enterprise-wide data architecture to support delivery of business objectives
• Be a key stakeholder and advisor in all new strategic data initiatives and ensure alignment to the enterprise-wide data strategy; drive a target and consistent data architecture across the organization; develop and document architecture specifications that serve as the reference for engineering and execution
• Define data architecture standards, guidelines, and best practices; build a framework of principles to ensure data integrity across the business (including but not limited to Core Systems, ERP, CRM, Data Lake-house, Data Fabric, external interfaces, etc.)
• Ensure that the data architecture and roadmap is aligned to the business and technology strategies.
• Build and maintain appropriate Enterprise Architecture artefacts including; Entity Relationship Models, data dictionary, taxonomy to aid data traceability
• Provide technical oversight to solution delivery in creating business driven solutions adhering to the enterprise architecture and data governance standards

We are committed to a safe and healthy environment for our employees & customers and will require all prospective employees to be fully vaccinated.

Requirements

• Bachelor’s degree in IT, Computer Science, Software Engineering or equivalent.
• Experienced technology leader with 10+ years of data architecture experience
• Proven experience in architecting and implementing Business Intelligence and Data warehouse platforms, Master Data Management, data integration and OLTP database solutions.
• Possess in-depth knowledge of and able to consult on various technologies
• Strong knowledge of industry best practices around data architecture in both cloud based and on prem solutions.
• A comprehensive understanding of the principles of and best practices behind data engineering, and the supporting technologies such as RDBMS, NoSQL, cache, in-memory stores, Graph DB, Hadoop and Spark
• Experience of architecting data solution across hybrid (cloud, on premise) data platforms.
• A comprehensive understanding of data warehousing and data transformation (extract, transform and load) processes and the supporting technologies such as Amazon Glue, EMR, Azure Data Factory, Data Lake, other analytics products
• Excellent problem solving and data modelling skills (logical, physical, sematic and integration models) including; normalisation, OLAP / OLTP principles and entity relationship analysis
• Experience of mapping key Enterprise data entities to business capabilities and applications