Agnes Liu

Join us in building world-class AI products for Google Finance and for many CFO organizations in the world. Full details below!

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We're hiring three AI Analytics Engineers on the Data Science & Analytics team at Airtable. These are early to mid-career roles for builders who are excited about working at the intersection of data science, analytics, and AI. One of these roles will report directly to me as we spin up a new AI & Analytics Platform function. Things are moving fast. The most impactful work in data right now isn't building models — it's fundamentally changing how a company gets answers from its data. That's where my focus is, and it's why Airtable is investing in a new team to own it. Over the past six months, our team stopped experimenting with AI on the side and restructured around it: - Agent-powered development workflows — I built shared AI systems for the data team embedded in how we write SQL, build pipelines, document business logic, and debug. Team members are regularly seeing 2x to 10x speed improvements — not from shortcuts, but from AI woven into every step of how we build. - Conversational analytics — Ad-hoc questions that used to fill our queue from leadership, product, GTM, and finance are increasingly answered by an AI agent — in natural language, where people already work. -A context layer that makes it trustworthy — We built a system that gives the agent real business knowledge — which table is correct for a given analysis, how metrics differ across teams, why something spiked last quarter. Context that usually lives in an analyst's head. - A learning loop that compounds — Every user correction becomes a permanent improvement, not a one-off fix. — We're doubling down. Airtable is standing up an AI & Analytics Platform function within DS&A to keep building at the intersection of data science, ML, analytics, and AI — and I'm leading it. We need more builders. AI Analytics Engineer, AI & Analytics Platform First hire on this team. You'll build the context layer that makes AI tools accurate, design evaluation frameworks that make them trustworthy, and develop agent systems that let the org self-serve with confidence. You'll work directly with me to shape what this function becomes. https://lnkd.in/gFstQqQb We're also hiring two roles embedded in other parts of the business: AI Analytics Engineer, Marketing Analytics Own data infrastructure, dashboards, and AI-native tooling for Marketing. Build the canonical data sources leadership relies on. https://lnkd.in/gYHS55ma AI Analytics Engineer, Business Analytics Own financial data models powering ACV, ARR, billings, and revenue. Partner with a Finance team that's building their own AI agents and workflows. You'll build the data foundation they run on. https://lnkd.in/gV5hnJjG — I'm looking for people who build before they're asked. Who have a side project that started as "I wonder if I could..." and turned into something real. Who pick up a new tool and immediately try to break it. If that sounds like you — reach out or apply. I'd love to talk.

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Bias in Causal Inference | Framing the problem When you can’t run an A/B test and rely on observational data to understand the impact of initiatives (e.g., promotions or new feature introductions), careful problem framing and awareness of bias are essential. Two common biases in causal inference are selection bias and confounding bias. Here’s a summary on the former, selection bias: Selection Bias | Conditioning on an Effect (Collider Bias) Selection bias from conditioning on an effect arises when we restrict the analysis to a variable influenced by treatment. For example, measuring a promotion’s impact on cancellations only among purchasers introduces bias. The promotion increases purchases, and purchase intent affects both purchases and cancellations, leading to misstated impact estimates. Problem framing depends on the outcome to influence. For example, to reduce overall churn, measure the total effect across all exposed users, including non-purchasers, capturing funnel-level influence. For post-purchase interventions, measure the conditional effect among purchasers. MVP causal representation: Promotion → Purchase ← Purchase Intent (unobserved) Purchase → Cancellation TLDR: Measure total cancellation impact for broad churn prevention strategies OR conditional impact among purchasers for tactical post-purchase interventions. Selection Bias | Conditioning on a Mediator Selection bias from conditioning on a mediator occurs when we control for a variable on the causal path from treatment to outcome. For example, notifications influence engagement, which impacts retention. Controlling for engagement removes part of the effect, giving an incomplete estimate. For attributing retention impact from notifications, focus on the total effect, capturing both direct impact and that flowing through engagement. To understand how much retention is driven by engagement, measure the mediated effect, which helps guide targeted engagement-driving initiatives without conflating total impact. MVP causal representation: Notification → Engagement (Mediator) → Retention Notification → Retention (direct) TLDR: Measure total impact on retention for measuring the success of feature introduction (e.g., notifications) OR the mediated impact from engagement to retention for targeted engagement strategies. Note: All opinions are my own and shared for data science community discussion.

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"We can just plug in the historical data right?" My head: "No, because it's captured differently with different segmentation, at a detail level we don't support through two different systems with inconsistent naming conventions and saved in CSVs on someones hard drive but we don't remember who it is, except for the weeks they went on PTO which are just missing and can't be replaced." My mouth: "We'll look into it."

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Navigating Snowflake's Q3 Earnings and AI-Driven Future 🚀 In the past few days, Snowflake has been making waves, both for the performance it delivered in its Q3 earnings and the exciting AI-driven collaborations that lie ahead. On December 12, 2025, Snowflake reported earnings that exceeded analysts’ expectations, with Q3 FY2026 revenue hitting $1.21B, an 8% YoY growth. Despite the strong performance, stock movements post‑earnings have been volatile, with some concerns about growth deceleration in the coming quarters. These concerns stem from more conservative forecasts and the increased competition in the data cloud space. However, amid this short-term market turbulence, the future outlook for Snowflake remains exciting — AI-driven advancements are at the core of its strategy. Snowflake’s deepening partnership with Anthropic is a $200 million multi-year investment, emphasizing AI-native workflows and language models (LLMs), which further positions the company as a leader in AI‑powered data infrastructure. The strength of Snowflake’s platform in integrating AI solutions with data operations offers a glimpse into how future enterprises will optimize and drive business intelligence. Key Highlights from Snowflake’s Recent Performance: 🔹 Strong Q3 Performance, with revenue exceeding expectations 🔹 AI & Data-Driven Future: $200M partnership with Anthropic to boost AI capabilities 🔹 Price Volatility: Market uncertainty following earnings, but long-term potential remains intact Why This Matters: In the broader context of enterprise technology and AI-driven innovation, Snowflake is paving the way for how data infrastructure will evolve to support AI, machine learning, and large-scale automation. As businesses across sectors invest heavily in cloud computing, AI, and data analytics, Snowflake’s platform is positioned to be at the heart of this transformation. Looking ahead, the true question for Snowflake and other players in this space is: How can we balance strong near-term execution with the longer-term vision of AI-driven data ecosystems? If you’re following the evolution of data, AI, and cloud infrastructure, it would be exciting to hear your thoughts on the future role of companies like Snowflake in shaping AI‑powered businesses. How is your organization planning for the AI-driven future?

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As a BI analyst, I've always worked with data that's already clean and ready. But how does it get there? That curiosity led me to build my first data engineering project, and wow... I learned SO much. What I Built: A stock data pipeline that processes both real-time and historical data. It's not perfect, but it works, and more importantly - I understand how it works now! The pipeline: → Pulls live stock quotes through Kafka → Loads historical data in batches with Airflow → Transforms everything with DBT → Serves data to Power BI dashboards (finally, my comfort zone 😅) My Learning Journey: Coming from BI/analytics, I had to learn: - How data actually flows before it reaches my dashboards - Why sometimes "real-time" isn't always necessary (batch is often smarter) - Setting up infrastructure with Docker (lots of trial and error) - Writing Python beyond pandas (Kafka producers/consumers were intimidating!) - Understanding the difference between data engineering and analytics Honest Moments: ❌ Kafka confused me for days (still learning tbh) ❌ Made rookie mistakes with data types and timezones ❌ Spent hours debugging Docker containers ✅ But each error taught me something valuable What Surprised Me: You don't need to be a "senior engineer" to build data pipelines. Tools like Docker and Airflow abstract away so much complexity. If you understand SQL and basic Python, you can start learning data engineering. Why I'm Sharing This: If you're a BI analyst, data analyst, or anyone curious about the "upstream" side of data - this project showed me it's totally approachable. You already understand the data modeling part (Silver/Gold layers felt natural!), it's just learning the ingestion and orchestration pieces. I documented everything - including my mistakes and "aha!" moments - in this Medium article: 💻 GitHub : https://lnkd.in/ggFhWMF3 📝 Read my learning journey: https://lnkd.in/gWydqG43 For other BI/analytics folks exploring data engineering: What's holding you back? Happy to share what helped me get started! #DataEngineering #BIAnalyst #CareerTransition #LearningInPublic #DataAnalytics #ModernDataStack #TechLearning #DataScience #Kafka #Airflow #DBT

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LLMs are everywhere and I believe one of the biggest challenges right now is aligning them to real-world utility Some thoughts from recent work: - Fine-tuning is great, but prompt engineering still wins for many lightweight use cases. - Evaluating with user-facing metrics (not just accuracy or perplexity) changes how you build. - Bias & fairness issues pop up even with apparently clean data. If you’re working with LLMs, what evaluation metrics do you prioritize, and why?

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3rd Normal Form vs. Star Schema: It’s not just about the "process." It’s about the physics of data storage. 💻 If you've ever wondered why your analytical queries are slow or why your transactional databases feel complex, it comes down to one choice: Are you protecting the data, or are you presenting it? I’ve summarized the e-commerce journey from Shopping Cart to Dashboard in my latest Medium post. #Analytics #DataEngineer #BigData #Medium

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