Data Engineering Manager
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Summary
Recruiting on behalf of an established client, Eames Consulting Group seeks a Data Engineering Manager in Singapore to lead and mentor a data engineering team, architect a cloud-based data lakehouse, drive zero-disruption legacy migrations, and embed governance (GDPR) into pipelines using tools like Airflow and dbt.
We are partnered with an established firm to look for a Data Engineering Manager.
Key Responsibilities • Provide leadership, mentoring, and technical direction to a high-calibre data engineering team while fostering engineering best practices, CI/CD, and system observability. • Lead the architectural design and scaling of a modern cloud-based data lakehouse and transformation framework processing high-volume consumer behavioural events and revenue streams. • Direct system migrations from legacy data setups to next-generation distributed storage and compute frameworks with zero business disruption. • Embed automated data governance, cataloguing, lineage tracking, and regulatory compliance protocols (GDPR/privacy frameworks) directly into data pipelines.
Requirements • Bachelor's or Master's degree in Computer Science or a relevant STEM discipline. • At least 5 years of engineering people management or technical team leadership experience. • At least 10 years of overall technical background building scalable distributed data systems and robust software solutions. • Deep hands-on knowledge of cloud data engineering, lakehouse storage layers, orchestration tools (e.g., Airflow), and data transformation engines (e.g., dbt). • Strong track record in establishing automated testing, infrastructure-as-code, streaming/batch ingestion, and governance frameworks. • Solid commercial understanding of cloud cost optimization (FinOps) and translating technical architecture into product/business impact.
Key Responsibilities • Provide leadership, mentoring, and technical direction to a high-calibre data engineering team while fostering engineering best practices, CI/CD, and system observability. • Lead the architectural design and scaling of a modern cloud-based data lakehouse and transformation framework processing high-volume consumer behavioural events and revenue streams. • Direct system migrations from legacy data setups to next-generation distributed storage and compute frameworks with zero business disruption. • Embed automated data governance, cataloguing, lineage tracking, and regulatory compliance protocols (GDPR/privacy frameworks) directly into data pipelines.
Requirements • Bachelor's or Master's degree in Computer Science or a relevant STEM discipline. • At least 5 years of engineering people management or technical team leadership experience. • At least 10 years of overall technical background building scalable distributed data systems and robust software solutions. • Deep hands-on knowledge of cloud data engineering, lakehouse storage layers, orchestration tools (e.g., Airflow), and data transformation engines (e.g., dbt). • Strong track record in establishing automated testing, infrastructure-as-code, streaming/batch ingestion, and governance frameworks. • Solid commercial understanding of cloud cost optimization (FinOps) and translating technical architecture into product/business impact.