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Open nowPosted 15 days ago

Director, Data Engineering

Workable (global search)415 open roles

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TELECOMMUTE
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Your applicationOpen nowDirector, Data EngineeringWorkable (global search) · TELECOMMUTE

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This job: posted 15 days ago

The posting

As Director, Data Engineering you'll take charge of data across the whole Articore Group: Martech Engineering, Core Data Engineering, Analytics Engineering, and Data Science, each already running with its own manager. Your job is to turn four teams pulling in their own direction into one coherent strategy, one set of standards, and one voice Execs can trust, without stripping away what makes each team work.

You'll partner closely with our Melbourne-based Product Director for Data, and report to our SVP of Engineering.

Here's the real opportunity: every function has built exactly what it needed, locally, and now the group needs someone to build the global picture. Reliable pipelines with real SLAs. A warehouse finally consolidated across four brands instead of four different ways of doing things. A genuine plan to pay down the technical debt sitting underneath all of it. This is architecture at group scale, and you're the one drawing the blueprint.

The job is holding the tension between what the business needs right now and the long-term rationalization that makes everything else possible, without dropping either ball.

Core Responsibilities

You'll partner with Executives and stakeholders across Product, Marketing, and Analytics, owning your organization's roadmap end-to-end across three areas: Delivery, People, and Technology.

Delivery

  • Set direction for data technology across the group, coordinating roadmaps across data engineering, analytics engineering, martech, and data science to unlock key business initiatives.
  • Own prioritization across OKR-driven initiatives, ad-hoc requests, and platform work — owning intake and planning, and making the case for tradeoffs directly to the executive team.
  • Build the architecture behind reliable, business-critical pipelines: SLAs per layer, and technical debt retired to the point teams can trust the data they run the business on.
  • Coordinate with adjacent teams, including product and other engineering functions, to align roadmaps and land cross-functional initiatives.

People

  • Hire and build out the data organization, including key hires across Australia, the US, and India.
  • Manage engineering managers directly, with skip-level visibility into technical leads. Set clear ownership and handoff practices so no region waits on one person or timezone.
  • Partner with each manager on the practices that make execution predictable — sprint planning, delivery rituals, and reporting.
  • Own performance, development, and comp conversations at the manager layer, and calibrate standards across four functions without erasing what makes each work.

Technology

  • Steward technical direction across the full data stack, and consolidate technology to reduce cost while improving reliability and simplifying systems.
  • Drive architectural decisions on data flows between systems, the warehouse, and downstream analytics — balancing reliability, scalability, and delivery speed.
  • Own data governance and privacy practices — classification, retention, and data subject requests — aligned to GDPR, CCPA, and equivalent frameworks.
  • Champion AI across the teams, establishing efficiencies through agentic workflows and automation.
  • 8+ years leading data organizations, including at least 3 years managing managers.
  • Experience leading engineering teams through technological consolidation, including via an acquisition or merger.
  • Experience owning distinct engineering functions with different stakeholders — martech, data engineering, analytics engineering, and data science.
  • Direct ownership of a cloud, warehouse, or infrastructure budget, with a clear line of sight into spend, drivers, and savings.
  • Practical experience with data privacy and compliance frameworks (GDPR, CCPA), and familiarity with the modern data stack (dbt, Airflow, Snowflake).
  • A track record of consolidating a fragmented data estate through a full warehouse or platform migration. Experience managing Data Science or ML teams is a plus.

Key Attributes for Success

  • AI-first: demonstrable, current experience using agentic AI tooling in production engineering work.
  • Experience building high-performing global teams across time zones, with clear 'follow the sun' handoffs.
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