Lead, Data Engineering at Interswitch

Job Summary

• To oversee and manage data integration, and transformation processes within the Industry Ecosystem business of Interswitch, to enable data-driven decision-making, ensure data quality and accessibility, and drive operational efficiency by managing and supporting data engineers and scientists to deliver reliable and scalable data solutions that provide timely and accurate insights to support business objectives.

Responsibilities
Functional Implementation:

• Collaborate with other data teams to establish and maintain data integration processes to consolidate data from diverse sources, ensuring seamless data flow, data integrity, and a unified view of data across the organization.
• Lead the development, optimization, and maintenance of data pipelines, implement data cleansing and enrichment techniques, and establish data quality standards to ensure the availability of reliable, accurate, and usable data for decision-making.
• Oversee the end-to-end execution of data science projects, including problem formulation, data acquisition, exploratory data analysis, model development, validation, and deployment, ensuring timely and high-quality project delivery.

Data Governance and Compliance:

• Lead the establishment and enforcement of data governance policies, implement data security measures, promote ethical data handling and usage, ensure compliance with privacy regulations, and maintain data lineage and documentation, to protect data assets and maintain regulatory compliance.

Collaboration and Leadership:

• Build and lead a skilled data engineering team, providing guidance, mentorship, and support to foster their growth, ensuring the team's effectiveness and productivity in delivering data engineering projects.
• Define and execute a data engineering strategy, including identification of key focus areas, resource allocation, and timeline, to drive the organization's data-driven initiatives and achieve desired outcomes.
• Foster collaboration with stakeholders to understand business requirements, establish effective communication channels, and deliver data-driven insights and recommendations that inform decision-making and drive business outcomes.

Continuous Improvement and Innovation:

• Identify opportunities for process improvement, evaluate and implement new tools and technologies, encourage innovation, and stay updated with emerging trends in data engineering, ensuring continuous improvement and innovation within the data engineering function.
• Identify and implement a comprehensive training and development plan for direct reports, including identifying skill gaps, providing relevant training opportunities, mentoring, and coaching, resulting in the continuous improvement and growth of the data engineering team members.

Education
General Education:

• BSc in Computer Science / Engineering or related fields.

Evidence of strong industry/sector participation and relevant professional certifications such as:

• Azure Data Engineer Associate
• Azure AI Fundamentals - Databricks Certified Data Engineer Professional
• Databricks Certified Machine Learning Associate
• Amazon Web Services (AWS) Certified Data Analytics Specialty
• Google Professional Data Engineer
• Data Science Council of America (DASCA) Senior Big Data Engineer
• Certified Analytics Professional (CAP).