DataOps Engineer Manager

Client Overview:

Our client is collaborating with a leading digital bank that is transforming the financial industry through innovative and secure tech-driven banking solutions. This forward-thinking institution prioritizes a personalized and seamless user experience, establishing itself as a leader in digital transformation within the banking sector. Join a team dedicated to shaping the future of modern banking.

Job Purpose:

The DataOps Engineer Manager will design, build, and maintain data pipelines and tools that promote data democratization across the organization. This role involves working closely with Data Scientists, Product teams, and business stakeholders to convert business requirements into efficient and reliable data solutions that support data-driven decision-making.

Key Responsibilities:

• Develop and maintain data Lakehouse solutions utilizing Google BigQuery AI, ensuring performance optimization and cost-effectiveness for advanced analytics.
• Design and implement robust data pipelines (streaming, batch, API endpoints) for efficient data collection, transformation, and analysis.
• Ensure that data solutions adhere to security and compliance standards, facilitating detailed reporting and self-service analytics.
• Implement best practices for data quality, consistency, and integrity across the data infrastructure.
• Establish monitoring, logging, and alerting frameworks to maintain data pipeline reliability and performance.
• Collaborate with stakeholders to translate business needs into technical data solutions.
• Conduct code reviews and promote continuous improvement in development processes.
• Enable secure data sharing through data marketplaces while maintaining data catalogs and lineage.
• Document data solutions to ensure operational knowledge is accessible organization-wide.
• Mentor and guide a team of data engineers, fostering innovation and collaboration.
• Perform any other duties and responsibilities as assigned.
• Ensure compliance with necessary controls and information security policies, procedures, and training within departmental activities.

Preferred Qualifications:

• Tertiary level qualification from a recognized institution.
• Google Cloud Professional Data Engineer certification.
• Years & Nature of Experience:
• 3 to 5 years of experience demonstrating competencies in designing and implementing data pipelines using Google Cloud or other public cloud platforms.
• Expertise in Google Cloud technologies, including BigQuery, Data Fusion, DataForm, DataFlow, Cloud Composer, DataPlex, and Dataproc.
• Strong understanding of data governance principles, including security, compliance, and privacy by design.

Technical:

1. Data analysis
2. Data pipeline development

Behavioural:

1. Problem-solving
2. Effective communication
3. Collaboration

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