Bachelors degree in a relevant field and extensive experience in systems development and project management.
Academic background and experience in selected area of research required, or other applicable academic field as evidenced by an advanced degree in data science, computer science, statistics, or a related field, with a strong academic background in systems development. Ideal candidates will possess a robust understanding and demonstrated experience in the following areas:
Data Quality Assurance: Extensive experience in implementing and overseeing data quality control measures at the point of data entry. Candidates should demonstrate a strong ability to ensure that all incoming data adheres to established quality standards, including accuracy, completeness, and consistency. Experience in setting up foundational data quality and assurance processes is crucial.
Data Governance and Management: Proven expertise in managing complex data environments, including both data lakes and data warehouses. Candidates should have a solid background in data governance, with skills in managing access to data, ensuring compliance with data policies and regulations, and maintaining data lineage and metadata management. This experience is vital for upholding the integrity and usability of data throughout the organization.
Data Processing and Optimization: Demonstrated capability in overseeing data staging and processing activities. Applicants should have hands-on experience in data cleansing, deduplication, and validation processes. A keen understanding of how to prepare high-quality data for further processing and storage, making it usable for both operational and analytical purposes, is preferred.
Systems Integration: Strong technical proficiency in managing and maintaining database and datalake structures. Experience in seamlessly integrating and maintaining accessibility of data across various platforms and systems within an organization is essential. This includes the ability to oversee the integration of new data sources and maintain the structural integrity of existing databases and datalakes.
Snowflake: Experience with Snowflake's cloud-based data warehousing and analytics platform, including designing and developing data pipelines, creating and managing databases, and optimizing query performance.
AWS: Familiarity with Amazon Web Services (AWS), including cloud architecture, security, and compliance requirements. Experience with AWS services such as EC2, S3, Lambda, and DynamoDB.
Data Transformation: Proven experience with data transformation and data quality processes, including data cleansing, data mapping, and data validation. Familiarity with data transformation tools and techniques, such as data profiling, data standardization, and data aggregation.
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