Engineering Manager

• Lead high-performing teams of data engineers and developers to build robust, scalable data platforms and intelligent data products.
• Provide architectural guidance and support across cloud -native data engineering, including ELT pipelines, data lakes/warehouses, streaming systems, and API-first applications.
• Champion engineering excellence, modern DevOps practices, and automation across the lifecycle.

• Own end-to-end delivery of complex data initiatives—scoping, planning, execution, and go-live—aligned with SLAs and client expectations.
• Manage multiple concurrent workstreams using Agile/Scrum or hybrid delivery methodologies.
• Track delivery KPIs (velocity, quality, cost) and ensure continuous improvement of engineering processes and execution frameworks.

• Conduct regular capacity planning to ensure optimal team structure as per project demands and SLA commitments.
• Define clear roles, responsibilities, and performance benchmarks for team members.
• Design and implement competency development programs, including technical upskilling, mentoring, and personalized growth paths.
• Lead hiring & staffing efforts, support candidate evaluation, and prepare shortlisted candidates for client interviews.
• Ensure smooth onboarding, project orientation, and KT for new joiners.
• Maintain regular people connects to assess motivation, understand aspirations, and proactively address attrition risk.
• Foster cross-team collaboration and knowledge sharing culture.

• Participate in client meetings, especially during discovery, requirements gathering, and solutioning discussions.
• Contribute to effort estimation, scoping, and delivery planning for new opportunities and expansions.
• Make presentations on project status, delivery KPIs, and risk mitigation during regular cadence calls.
• Present monthly and annual performance reports to senior client and internal leadership teams.
• Act as the technical bridge between client stakeholders and internal teams, ensuring transparency, alignment, and trust.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Need to have 8+ years of hands on experience in software/data engineering background, including 5+ years in data engineering
• At least 2 years in a leadership or managerial role post the hands-on experience.
• Proven success in delivering complex, cloud-native data platforms and products at enterprise scale.

• Proficiency in Python, SQL, and cloud-native data engineering (preferably GCP).
• Strong hands-on experience with GCP services: BigQuery, Dataflow, Cloud Storage, Pub/Sub, Composer, GKE or any equivalent services from AWS can be considered.
• Experience designing and deploying ETL/ELT pipelines, APIs using Flask/FastAPI, and data product architectures.
• Familiar with CI/CD, Git workflows, and project management tools (e.g., Jira, Confluence).
• Experience designing and implementing AI/ML-powered applications, especially around data-driven personalization, recommendations, or predictive analytics will be highly preferred
• Understanding of MLOps practices and integration of machine learning pipelines into data engineering workflows (e.g., using Vertex AI, Kubeflow, or MLflow).
• Proven experience in project tracking, estimation, stakeholder reporting, and client-facing communications.

• Experience in multi-region delivery teams and global stakeholder coordination.
• Exposure to data governance, compliance, or privacy frameworks (GDPR, HIPAA, etc.).

• Excellent verbal and written communication skills across all stakeholder levels.
• Strong presentation, negotiation, and relationship management capabilities.
• Empathetic leadership, with the ability to motivate, coach, and develop high-performing teams.
• Solution-oriented mindset with high ownership and accountability.

dentsu