“I have had the pleasure of working with Fi very closely during her time at Linktree. Fi is an excellent leader who leads with empathy and influence. She led the ML team at Linktree, pioneered the ML strategy and drove strong business outcomes in the space. She actively supports her teams and creates a safe space where people feel heard and are empowered to do their best work. Fi is highly skilled in her craft and is an outstanding people leader. I cannot recommend Fi highly enough!”
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you can't vibe code healthcare
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Something BIG is coming! We’re teaming up with none other than Professor Andrew Gelman from Columbia University for an exclusive webinar that dives…
Something BIG is coming! We’re teaming up with none other than Professor Andrew Gelman from Columbia University for an exclusive webinar that dives…
Liked by Fi Milne
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Please, stop building RAG projects in 2025! If you want to succeed as an AI engineer, focus on this instead: - solve a real problem - build a…
Please, stop building RAG projects in 2025! If you want to succeed as an AI engineer, focus on this instead: - solve a real problem - build a…
Liked by Fi Milne
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Ravi Lathar
🇦🇺 The Australian data engineering market is quietly going through a major shift. A few years ago (or on-prem systems), success meant: • Moving data from A → B • Running nightly ETL jobs • “Keeping the pipelines alive” Today, expectations are very different- 🔹 Cloud-native over lift-and-shift 🔹 ELT & streaming over batch-only ETL 🔹 Governance, lineage & security as default 🔹 Cost optimisation as a core engineering skill 🔹 Data engineers enabling AI, not just reporting In Australia, regulation (APRA, CPS 234, privacy laws) + cloud maturity is forcing teams to treat data platforms as products, not projects. The most in-demand data engineers now: 1. Understand Spark & SQL deeply 2. Think in platforms, not pipelines 3. Communicate well with analytics, governance & business teams 4. Own data quality and cost "The role isn’t disappearing — it’s evolving." Databricks | Microsoft | Google | Amazon NGM Group
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Jake Griffiths
🚜➡️📊 From dairy farms to data platforms! This week on Keeping Up With Data, Seán and Polain sit down with Tim Armstrong, Director of Data, Technology & Product at Nova Entertainment. Tim shares his journey from rural New Zealand to the forefront of media tech & how lessons from the farm are still driving his decision-making today. The guys dig into: 🧠 The power of Customer Data Platforms (CDPs) 🔁 The shift from DMPs to CDPs 🛡️ Privacy, governance & real-time analytics 📈 Why Nova’s CDP sits at the heart of its digital strategy 🎧 Whether you’re a data pro or just CDP-curious, this one’s a cracker. Listen now 👇
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Mark Drasutis
According to new research from Amplitude, many Australian professionals trust their gut more than their data when making decisions. Why is this still happening in 2025?! The answer lies in the barriers to getting started. Analytics products can be complicated, and people don't have enough time to learn how to use yet another tool. The good news? It doesn’t have to be that way. By simplifying analytics products into one simple, consolidated platform, and providing the education you need to get started, relying on data can be easy. Learn more: https://lnkd.in/g7Ja6qV5 #leadership #strategy #data #datadecisions #productanalytics #customercentric #operatingmodel #plg #growth #marketinganalytics #customerdata #jtbd #productmanagement #datadriven
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Rohan Light
An important update for colleagues delivering #datagovernance here in NZ: the guidance for Māori data associated to the new Information Sharing Standard has now dropped. When the initial guidance came out the Māori data governance requirements embedded in the new standard led to a few second order questions. The initial supporting guidance developed the material in the standard, but that was only provisional. That guidance has since been chopped up into sections and expanded, E.g model legal clauses for updating contracts. I started analysing the implications of the four small rules in the standard in the beginning of June and only concluded about three weeks ago. Aka the implications are significant for the existing body of data governance regimes (broadly understood). Furthermore, the expectations are embedded in the standard so we can't pick and choose the bits we like/agree with and treat the remainder as optional Fortunately there are pathways to making all the various regimes synchronise but obtaining that solution necessitates changes in the order of operations (there is a BEDMAS for data governance). Usefully the standard elevates the Te Kāhui Raraunga framework up a level and that excellent body of work helps with the reframing and reordering.
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