ETL Lead

Job Purpose: 

To create and expand our data EDW architecture, as well as optimizing the DWH model. For Data pipeline building and data wrangling for optimizing data systems and building them from the ground up.

  

Key Accountabilities:

Designing, implementing, and maintaining data services, interfaces, and real-time data pipelines via the practical application of existing, new, and emerging technologies and data engineering techniques

Participate as lead engineer on projects and assist in communication/collaboration sessions with clients.

Design, develop and test data workflows/pipelines, schemas/extracts and data transformation to ensure integrity in data between source to target and other downstream systems.

Enhance, augment, and shape our data management strategy by acquiring and warehousing new data sources from inside and outside the organization.

Translate from technical to business, and vice versa. You need to be able to speak with the least technically-minded client (internal or external) and make technology make sense to them. Then turn around and do it the other way.

Actively contribute to the adoption of strong data engineering architecture, development practices, and new technologies.

Highly organized; able to organize and assimilate large quantities of complex information

Proactively develop and manage relationships with various stakeholders at the senior level and middle management level of the organization to understand reporting/Dashboards needs and requirements, including how they would like to change, modify, or add to a reporting load

Maintain up-to-date technical documentation while continuously delivering technical solutions and business impacts.

Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and other integration technologies.

Extensive experience in advance data management techniques like DQ, MDM, Metadata management, Data Modelling.

Work closing with data architecture and other units to define clear data integration strategy and standards.

 

Qualifications, Experience & Skills

Qualifications:

A bachelor's or master's degree in business administration, computer science, data science, information science or related field, or equivalent work experience

Experience:

5-8 Years in banking and technology

Skills:

• BS or master's in management information system, Computer Science, or Information Technology (or equivalent work experience to substitute for education)
• Minimum 5 to 8 years work experience in Data Engineering field.
• Minimum 3 to 5 years work experience in Banking and financial institutions products & services.
• Experience and technical background in Data Integration Technology and tools (like but not limited to: Microsoft SSIS, Talend, Informatica)
• Experience and technical background in Relational Databases, Data Warehouses and Data Manipulation Language DML (SQL) and various modelling techniques, especially dimensional modelling.
• Expertise in streaming & real-time data processing using a technology like Spark, Kafka, ksqlDB etc. and best practices on production deployment of these platforms

Qualifications, Experience & Skills

Qualifications:

A bachelor's or master's degree in business administration, computer science, data science, information science or related field, or equivalent work experience

Experience:

5-8 Years in banking and technology

Skills:

• BS or master's in management information system, Computer Science, or Information Technology (or equivalent work experience to substitute for education)
• Minimum 5 to 8 years work experience in Data Engineering field.
• Minimum 3 to 5 years work experience in Banking and financial institutions products & services.
• Experience and technical background in Data Integration Technology and tools (like but not limited to: Microsoft SSIS, Talend, Informatica)
• Experience and technical background in Relational Databases, Data Warehouses and Data Manipulation Language DML (SQL) and various modelling techniques, especially dimensional modelling.
• Expertise in streaming & real-time data processing using a technology like Spark, Kafka, ksqlDB etc. and best practices on production deployment of these platforms