We are seeking an experienced Data Engineer to design, build, and optimize scalable data pipelines and architectures. The role focuses on ensuring reliable data integration, transformation, and accessibility across multiple platforms, including cloud environments, to support business intelligence and analytics initiatives.
Key Responsibilities
Design, develop, and maintain scalable data architectures and data pipelines based on business requirements
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Own a focused portfolio, not a sprawling one. You'll carry a capped, ranked set of active initiatives (target: 2-3 max concurrently) with clear launch dates — depth over breadth.
Build, don't just spec. Prototype AI features directly — LLM-powered workflows, agents, automations — using tools like Python, LLM APIs, RAG pipelines, and no-code/low-code platforms — so initiatives can move without sitting in the dev backlog.
Lock scope before build starts. Get sign-off on a PRD before development begins; any new requirement after that goes through a lightweight change-request, not a silent scope add.
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You are measured on the pod's results, not just your own accounts. You lead your own portfolio and you are equally accountable for what your pod members deliver on theirs. That means coaching them, reviewing their work before it reaches the client, and stepping in on their accounts when the situation calls for it.
You work AI-native, not AI-curious. We are rebuilding this agency around AI-augmented workflows: reporting, insight extraction, deck production, campaign QA, creative ideation. You work inside that system, improve it, and teach it to your pod. Hard requirement.
Structure and optimise Meta accounts under current delivery logic: Advantage+, broad targeting, consolidation, and the signal quality that drives them
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