ETL Lead

Job Purpose

The role focuses on designing and expanding the enterprise data warehouse (EDW) architecture and optimizing data warehouse (DWH) models. The position entails building and optimizing data pipelines and handling data wrangling to establish efficient data systems from scratch.

Key Responsibilities

• Design, develop, and maintain data services, real-time data pipelines, and interfaces using both established and emerging data engineering technologies.
• Serve as lead engineer on various projects, facilitating collaboration and communication with clients.
• Create and validate data workflows, pipelines, schemas, extracts, and transformation processes to ensure data integrity from source through to target and downstream systems.
• Enhance data management strategies by incorporating and warehousing new data sources internally and externally.
• Translate technical concepts into business-friendly language and vice versa, ensuring clear understanding between technical teams and less technical stakeholders.
• Promote the adoption of robust data engineering architectures, development methodologies, and new technologies within the team.
• Organize and synthesize large volumes of complex information effectively.
• Build and maintain strong relationships with senior and middle management stakeholders to understand and accommodate their reporting and dashboard requirements.
• Keep technical documentation updated while continuously delivering impactful technical solutions.
• Develop infrastructure for optimal extraction, transformation, and loading (ETL) from diverse data sources leveraging SQL and other integration technologies.
• Apply advanced data management techniques such as data quality (DQ), master data management (MDM), metadata management, and data modeling.
• Collaborate closely with data architects and other teams to establish clear data integration strategies and standards.

Qualifications & Experience

• Bachelor's or master's degree in business administration, computer science, data science, information science, or related fields; equivalent professional experience is acceptable.
• 5 to 8 years of relevant experience in data engineering.
• 3 to 5 years of experience within banking and financial services sectors.

Required Skills

• Educational background in management information systems, computer science, or information technology or equivalent professional experience.
• Strong practical experience working with data integration tools such as Microsoft SSIS, Talend, or Informatica.
• Expertise in relational databases, data warehousing, SQL (data manipulation language), and dimensional data modeling techniques.
• Proficiency in streaming and real-time data processing platforms such as Apache Spark, Kafka, and ksqlDB, along with experience in deploying these technologies in production environments.