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Job Purpose and Impact

• The Senior Professional, Data Engineering job designs, builds and maintains complex data systems that enable data analysis and reporting. With minimal supervision, this job ensures that large sets of data are efficiently processed and made accessible for decision making.

Key Accountabilities

• DATA INFRASTRUCTURE: Prepares data infrastructure to support the efficient storage and retrieval of data.
• DATA FORMATS: Examines and resolves appropriate data formats to improve data usability and accessibility across the organization.
• DATA & ANALYTICAL SOLUTIONS: Develops complex data products and solutions using advanced engineering and cloud based technologies, ensuring they are designed and built to be scalable, sustainable and robust.
• DATA PIPELINES: Develops and maintains streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.
• DATA SYSTEMS: Reviews existing data systems and architectures to identify areas for improvement and optimization.
• STAKEHOLDER MANAGEMENT: Collaborates with multi-functional data and advanced analytic teams to gain requirements and ensure that data solutions meet the functional and non-functional needs of various partners.
• DATA FRAMEWORKS: Builds complex prototypes to test new concepts and implements data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives.
• AUTOMATED DEPLOYMENT PIPELINES: Develops automated deployment pipelines improving efficiency of code deployments with fit for purpose governance.
• DATA MODELING: Performs complex data modeling in accordance to the datastore technology to ensure sustainable performance and accessibility.

Qualifications

• Minimum requirement of 4 years of relevant work experience. Typically reflects 5 years or more of relevant experience.

Preferred Qualifications:

• Experience with corporate-level data storage and processing platforms (AWS, Snowflake, etc.) and GenAI Tools
• Proven history in leading design, development, and maintenance of scalable ETL pipelines for multi-scale datasets
• Proven experience leading business partnership, requirements gathering, and agile execution in large-scale data development programs