Director of Data Engineering

At Suntory Global Spirits, we craft spirits of the highest quality and deliver brilliant experiences to people around the world. Suntory Global Spirits has evolved into the world's third largest leading premium spirits company... where each employee is treated like family and trusted with legacy. With our greatest assets - our premium spirits and our people - we're driving growth through impactful marketing, innovation and an entrepreneurial spirit. Suntory Global Spirits is a place where you can come Unleash your Spirit by making an impact each and every day.

This is a director-level leadership role with a clear mission: build a scalable, reliable and reusable data engineering capability that makes trusted data available when and where the business needs it.

The Director, Data Engineering sets the enterprise engineering strategy, operating model and standards. The role connects business priorities, data product roadmaps and reference architecture to a reliable portfolio of pipelines, data products and platform capabilities that move data securely from source to consumption.

You will lead the chapter of data engineers and ETL developers, deploying talent into cross-functional product and initiative teams. You will partner with Data Product Management, Platform and Architecture, Governance, BI, AI, Digital Products, Security, Privacy and business teams to ensure technical quality, predictable delivery and reliable operations globally.

The organization is developing a federated hub-and-spoke model: the central Data & Analytics Hub builds reusable capabilities and supports cross-functional initiatives, while brands, markets and functions own priorities, data domains and value realization. This role creates one engineering chapter and effective hand-offs without fragmenting architecture, standards or tooling.

Success is measured by data reliability, quality, freshness and availability; predictable delivery; faster source onboarding and data-product delivery; reuse, automation, talent strength and disciplined cost - not by the number of pipelines or volume of code produced.

The Director, Data Engineering leads the enterprise capability that collects, integrates, transforms and serves data at scale. The role sets strategy and standards, operates secure and reusable pipelines and products, develops the engineering chapter and turns priority outcomes into sustainable data capabilities.

• Develop the multi-year data engineering strategy and roadmap, aligned to Data & Analytics priorities with focus in commercial, data product roadmaps, reference architecture and platform capabilities; recommend investments and trade-offs to senior leadership.
• Translate the initiative and data product portfolio into transparent engineering capacity, resource and delivery plans, with clear dependencies, estimates, sequencing and commitments.
• Define the engineering component of the federated hub-and-spoke model, including chapter accountabilities, decision rights, engagement models, delivery hand-offs and support expectations.
• Partner with data product owners, product managers and business initiative owners in a two-in-the-box model to shape requirements, assess feasibility, inform business cases and sequence delivery around outcomes.
• Set the balance of internal talent, strategic partners and managed services, protecting critical enterprise knowledge while using external capacity and expertise effectively.
• Establish engineering metrics and operating rhythms for delivery, quality, reliability, reuse, productivity, cost and stakeholder outcomes; use evidence to improve priorities and execution.
• Oversee the design, build and operation of enterprise data pipelines and curated data products aligned to architecture, domain roadmaps, standards and service levels.
• Provide scalable patterns for ingestion, integration, transformation and serving across batch, streaming, APIs and events, selecting the simplest approach that meets business and operational needs.
• Drive automation and reuse through standardized frameworks, shared components and common data assets, reducing duplicated transformation and accelerating delivery across teams.
• Prepare fit-for-purpose data for BI, AI, digital products and self-service users, with clear models, definitions, quality criteria and consumer expectations.
• Own the lifecycle from discovery and source onboarding through build, testing, release, support and decommissioning, with clear ownership, documentation and service expectations.
• Set common practices for design, coding, version control, peer review, automated testing, CI/CD, environment management and release governance across internal and partner teams.
• Implement DataOps and observability for data quality, freshness, lineage, pipeline health, performance and cost; define service-level objectives, alerts and production ownership.
• Build resilient operations through incident, problem and change management, root-cause analysis, recovery planning, runbooks and continuous improvement.
• Automate orchestration, testing, deployment, infrastructure configuration and repetitive operations to improve productivity, speed and consistency while reducing manual risk.
• Manage performance, capacity and cloud consumption with Platform and Finance; optimize workload design and cost without compromising reliability, security or outcomes.
• Partner with Platform and Architecture to evolve reference patterns for integration, modeling, storage, processing and interoperability, translating the technology roadmap into engineering priorities.
• Embed data quality, metadata, lineage, cataloging, retention and master-data requirements into pipelines and products, automating governance wherever possible.
• Work with data owners, stewards and product owners to define data contracts, domain ownership, acceptance criteria and quality KPIs, routing issues to accountable owners.
• Collaborate with Security, Privacy, Legal and Risk to implement least-privilege access, encryption, secrets management, auditability and compliant processing by design.
• Govern architecture exceptions, technical debt and technology lifecycle decisions pragmatically, preventing tool proliferation and inconsistent patterns while enabling delivery at pace.
• Lead and develop the enterprise data engineering chapter, including engineering managers, data engineers and ETL developers; set objectives, career paths, capability plans, hiring standards and expectations.
• Deploy engineers into cross-functional Agile teams while maintaining chapter-based technical leadership, coaching, quality standards, communities of practice and knowledge sharing.
• Act as the senior data engineering interface with executives, business functions, Product Management, Architecture, Governance, BI, AI, Digital Products and Technology Operations; make progress, risks and decisions transparent.
• Lead strategic engineering partners and vendors across capability, delivery, commercial performance and knowledge transfer; assess emerging technologies for practical enterprise value.