Data Engineer

As a Data Engineer at iKhokha, your primary responsibilities include designing and maintaining data pipelines, optimizing data processing for efficiency, and collaborating with data teams to meet their data requirements. You’ll also be involved in integrating data from various sources, ensuring quality and integrity, and implementing data governance measures to ensure compliance.

To qualify for the Data Engineer position at iKhokha, you need a BSc in Computer Science, IT, or a related field. Additionally, having 3-5 years of experience in a data engineering role, particularly working with data teams and strong knowledge of databases, ETL processes, and cloud platforms like AWS or Azure, will be critical.

Essential skills for a successful Data Engineer at iKhokha include proficiency in SQL, ETL processes, familiarity with data modeling (like Kimball), and experience in cloud environments such as Snowflake or AWS. Additionally, good programming skills in Python and a solid understanding of data quality and testing are vital for this role.

To apply for the Data Engineer role at iKhokha, you should prepare an impressive CV highlighting your relevant experience and skills in data engineering. Visit our careers page for the application process and submit your application along with a cover letter that showcases your passion for data and the FinTech sector.

At iKhokha, the work culture is collaborative and dynamic, where innovation is encouraged. As a Data Engineer, you will be part of a supportive team dedicated to achieving meaningful results with a focus on continuous learning and development. We promote a friendly environment and flexibility in working arrangements.

iKhokha offers numerous opportunities for professional development for Data Engineers, including access to on-demand learning resources, opportunities for study leave, and mentorship from experienced leaders. This commitment ensures that you can enhance your skills while contributing to impactful projects.

As a Data Engineer at iKhokha, you’ll work with advanced technologies and tools such as SQL databases, ETL frameworks, cloud platforms like AWS or Azure, and Python for data processing. You’ll also have the chance to explore and implement the latest data engineering trends and technologies.

When responding, focus on specific projects where you designed and maintained data pipelines. Detail the tools and frameworks you utilized and how you ensured efficiency and reliability in data processing. Share the impact your work had on the organization’s data analysis capabilities.

Discuss approaches you've taken to monitor and validate data quality, like implementing automated testing, conducting data audits, and developing clear data governance policies. Highlight your commitment to maintaining high standards in data handling.

List the relevant tools you’ve worked with, such as SQL, Python, and various cloud platforms. Mention any experience you have with data visualization tools or data warehouse solutions, showing your versatility and readiness to tackle diverse challenges.

Talk about your active engagement with data engineering communities, attending workshops, online courses, and following influential bloggers or platforms that publish relevant trends and insights. Sharing specific examples can illustrate your dedication to continuous learning.

Describe a specific challenge you faced related to data processing or integration. Explain the steps you took to analyze the problem, the solution you implemented, and its positive outcomes for the organization, demonstrating your problem-solving skills.

Share examples of collaborations with teams like data scientists or business analysts. Discuss how you communicated technical concepts to non-technical users and how that fostered successful project outcomes, highlighting your collaboration skills.

Discuss your time management strategies, such as prioritizing tasks, utilizing agile methodologies, and collaborating closely with your team to meet project deadlines while maintaining quality. Provide a concrete example of how you navigated a time-sensitive project.

Explain your understanding of data modeling concepts, such as the Kimball methodology, and provide examples of projects where you implemented these techniques. Highlight how proper data modeling enhances data usability and supports business needs.

Describe your strategies for ensuring seamless data integration, such as defining clear data mappings, choosing the right ETL tools, and ensuring data consistency. Highlight how this improves accuracy and reliability in reports and analyses.

Explain your understanding of data governance, including data privacy, compliance, and security best practices. Discuss how you’ve implemented governance frameworks in previous projects, ensuring robust data management standards.