Snowflake Data Engineers

1. Designs and develops Snowflake and Azure Data Factory data pipelines that extract data from various sources, transform it into the desired format, and load it into the appropriate data storage systems. 
2. Collaborates with data architects, solution architects, data scientists, data analysts, and business areas to optimize data builds for data quality, security, and governance in a holistic manner.
3. Integrates data from different sources, including cloud and on-premise SQL and Oracle databases, data warehouses, APIs, and external systems. 
4. Ensures data consistency and integrity during the integration process, performing data validation and cleaning as needed.
5. Transforms raw data into a usable format by applying data cleansing, aggregation, filtering, and enrichment techniques. 
6. Optimizes data pipelines and data processing workflows for performance, scalability, and efficiency. 
7. Monitors and tunes data systems, identifies and resolves performance bottlenecks, and implements caching and indexing strategies to enhance query performance. 
8. Implements data quality checks and validations within data pipelines to ensure the accuracy, consistency, and completeness of data. 
9. Support the CDO in leveraging the value of enterprise information assets and of the analytics used to render insights for decision-making, automated decisions and augmentation of human performance. 
10. Ability to train and upskill staff in Cloud Data Engineering skillsets.
11. Ability to work in an Agile and CICD environment.
12. Establishes the governance of data and algorithms used for analysis, analytical applications, and automated decision-making.
13. Project management capacity in initiating, planning, executing, and controlling agreed work.

EXPERIENCE:
University Degree in Computer Science, Data Management, Data Analytics, IT Management or Software Engineering

A minimum of five years' relevant experience in Information Technology, of which at least three years should have been in managing the development or deployment of data analysis, management, and integration solutions. 

• Demonstrated ability in Apache technologies such as Kafka, Airflow, and Spark to build scalable and efficient data pipelines. 
• Demonstrated ability to design, build, and deploy data solutions that capture, explore, transform, and utilize data to support AI, ML, and BI  
• Expert in ETL languages/tools such as Python, SQL, R, SAS, or Excel
• Statistical or data analysis background is a must.
• Able to work with the Data Architect to design, build and deploy data products based on designed architecture 
• Proficiency in the design and implementation of modern data architectures and concepts such as cloud services (AWS, Azure, GCP) and modern data warehouse tools (Snowflake). 
• Expert with database technologies such as SQL, NoSQL, Oracle, Hadoop, or Teradata. 
• Ability to collaborate within and across teams of different technical knowledge to support delivery and educate end users on data products. 
• Expert problem-solving skills, including debugging skills, allowing the determination of sources of issues in unfamiliar code or systems, and the ability to recognize and solve repetitive problems. 
• Excellent business acumen and interpersonal skills; able to work across business lines at a senior level to influence and effect change to achieve common goals. 
• Ability to describe business use cases/outcomes, data sources and management concepts, and analytical approaches/options. 
• Able to understand and design conceptual and physical data models.
• Experience with Data Governance and Cataloguing Tools.
• Ability to translate among the languages used by executive, business, IT, and quant stakeholders.

REQUIRED LANGUAGE(S):
Fluency in English. Knowledge of the other Client official languages is an asset.