The Head of Data will lead the architecture, scalability, and reliability of the company's data ecosystem, focusing on building and optimizing data platforms for analytics, product intelligence, and AI initiatives. This hands-on leadership role involves defining enterprise-wide data architecture, overseeing scalable data pipelines, and managing a high-performing data engineering team.
About the role
About the Role
Seeking a technically strong Head of Data with a deep data engineering background to lead the architecture, scalability, and reliability of a modern data ecosystem. This is a hands-on leadership role responsible for building and optimizing data platforms that power analytics, product intelligence, and AI initiatives. The ideal candidate has built and scaled robust data infrastructure, understands distributed systems deeply, and can lead engineering teams while aligning data strategy with measurable business outcomes.
Key Responsibilities
- Data Architecture & Engineering Leadership (Primary Focus)
- Define and own enterprise-wide data architecture and long-term platform strategy.
- Design and oversee scalable, secure, high-performance data pipelines (batch and real-time).
- Lead development of modern cloud-based data platforms (lakehouse/warehouse architectures).
- Ensure reliability, observability, and performance optimization of data systems.
- Drive Infrastructure-as-Code, CI/CD, and DevOps best practices for data engineering.
- Evaluate and implement best-in-class tools across ingestion, orchestration, storage, and transformation layers.
- Platform Scalability & Reliability
- Build highly available, fault-tolerant distributed data systems.
- Establish SLAs, data reliability standards, and monitoring frameworks.
- Implement automated data quality validation and testing pipelines.
- Optimize cost-performance balance of cloud data infrastructure.
- Data Governance, Security & Compliance
- Implement secure data access controls and encryption standards.
- Establish governance frameworks, lineage tracking, and metadata management.
- Ensure adherence to data privacy and regulatory requirements.
- Analytics & Machine Learning Enablement
- Provide analytics and BI teams with clean, well-modeled, production-grade datasets.
- Partner with product and engineering teams to embed data into core applications.
- Enable ML teams with scalable feature stores and model-ready pipelines.
- Support MLOps best practices for model deployment and monitoring.
- Team Leadership & Culture
- Build and lead a high-performing data engineering organization.
- Establish engineering standards, code review processes, and architectural governance.
- Mentor senior engineers and define technical career progression frameworks.
- Foster a culture of ownership, reliability, and engineering excellence.
Required Qualifications
- 12+ years of experience in data engineering, backend engineering, or platform engineering.
- 5+ years leading and scaling data engineering teams.
- Proven experience architecting and operating large-scale distributed data systems.
- Deep expertise in:
- Cloud platforms (AWS, GCP, or Azure)
- Data warehousing & lakehouse architectures
- ETL/ELT frameworks and orchestration tools
- Streaming technologies (e.g., Kafka or equivalent)
- SQL and Python (or comparable backend languages)
- Infrastructure-as-Code (Terraform, CloudFormation, etc.)
- Containerization and orchestration (Docker, Kubernetes)
- Strong understanding of system reliability, scalability, and cost optimization.
- Experience implementing data governance and security frameworks.