Data Architect

About the job
Minimum Position Qualifications:

• Extensive knowledge of data principles, patterns, processes, and practices
• Any experience defining evolutionary data solutions and underlying technologies
• Strong analytical skills with the ability to collect, organize, analyze, and disseminate

significant amounts of information with attention to detail and accuracy
• Experience with SQL database design, data modeling
• Analyze and organize raw data
• Build data systems and pipelines
• Conduct complex data analysis and report on results
• Demonstrated written and oral communication skills
• Basic understanding of network and data security architecture
• Strong knowledge of industry trends
• Knowledge of the following technical disciplines: data warehousing, data management,

analytics development, data science, application programming interfaces (APIs), data

integration, cloud, servers and storage, and database management
• Experience with SQL and NoSQL applications on Big Data Platforms
• Experience with Azure Data Platform stack: Azure Data Lake, Azure Synapse, Data Factory

and Databricks
• Experience with Python, Spark and SQL
• Any experience with streaming technologies like Kafka, IBM MQ and EventHub

Desired Previous Experience

• Any experience with operational data science, machine learning or artificial intelligence

solutions
• Any experience with data science solutions or platforms
• Data engineering certification (e.g IBM Certified Data Engineer) is a plus
• Any experience with data science solutions or platforms

Key Responsibilities

Essential Job Functions

• Drive digital innovation by leveraging innovative new technologies and approaches to

renovate, extend, and transform the existing core data assets, including SQL-based,

NoSQL-based, and Cloud-based data platforms
• Define high-level migration plans to address the gaps between the current and future state
• Present opportunities with cost/benefit analysis to leadership to shape sound architectural

decisions
• Lead the analysis of the technology environment to detect critical deficiencies and

recommend solutions for improvement
• Interpreting data, analyzing results using statistical techniques
• Developing and implementing data analyses, data collection systems and other strategies

that optimize statistical efficiency and quality
• Acquiring data from primary or secondary data sources and maintaining databases
• Promote the reuse of data assets, including the management of the data catalog for

reference