First DET Warsaw Meetup at Netflix
We are thrilled to host the inaugural DET Warsaw Meetup! Join us at Netflix's Warsaw office (in the Wola district) for an evening of learning, networking, and fun. Refreshments will be provided.
With limited capacity, please RSVP to secure your spot.
Netflix is hiring data engineers in Warsaw! Check out open positions here.
Cheers!
The DET Warsaw Team
Agenda
5:30 to 6:00 PM: Doors open, check in, and networking
6:00 to 7:00 PM: Talks and Q&A
7:00 to 8:00 PM: Closing remarks and networking
Speakers & Talks
Talk #1.1: Content Data Lifecycle at Netflix
Speaker: Inna Giguere (Director of Content Data Products at Netflix)
This talk focuses on content data lifecycle at Netflix, illustrating how data and analytics underpin every stage of content development, production, and distribution. Beginning with the ideation and pitch phase, we outline how data-driven insights inform script selection, talent acquisition, and deal negotiations. It then follows the journey through production planning, budget analysis, and post-production processes, demonstrating how predictive analytics and machine learning optimize resource allocation and scheduling.
The discussion further explores how content is prepared for launch—through localization, quality control, and promotional strategies—before shifting to post-launch analysis, where custom-built data products and visualizations measure audience engagement, performance, and member satisfaction. The presentation highlights the collaborative efforts of specialized teams in engineering, analytics, and product management, all working to build robust data foundations and tools that enable seamless decision-making throughout the content lifecycle.
By showcasing real-world examples such as budget analyzers, VFX cost predictions, and ratings automation, the presentation emphasizes Netflix’s holistic, data-driven approach to managing content from conception to audience impact, ultimately ensuring both economic value and member joy.
Talk #1.2: Infrastructure Data Products at Netflix
Speaker: Vivek Pasari (Manager of Security and Platform Data Engineering at Netflix)
This talk offers a behind-the-scenes look at Infrastructure Data Engineering at Netflix, revealing how foundational data products drive efficiency, security, and compliance across the platform. We’ll walk through the lifecycle of critical infrastructure data, from the creation of unified asset inventories and cost analytics to the management of privacy, security, and operational signals. You’ll learn how our team designs scalable data contracts and robust pipelines that power everything from cloud efficiency insights and vulnerability detection to regulatory compliance and external data requests.
In this session we’ll highlight the cross-functional partnerships between engineering, security, legal, and privacy teams, and share real-world examples—like cost attribution models, consent management, and incident response data flows—that illustrate our mission to deliver reliable, actionable data at scale. Whether you’re passionate about building resilient data systems or enabling smarter, safer decisions, this talk will show you how Infrastructure Data Engineering shapes the foundation of Netflix’s global platform.
Talk #2: Mastering Real-Time Anomaly Detection
Speaker: Olena Kutsenko (Staff Developer Advocate at Confluent)
Detecting problems as they happen is essential in today’s fast-moving, data-driven world. In this talk, you’ll learn how to build a flexible, real-time anomaly detection pipeline using Apache Kafka and Apache Flink, backed by statistical and machine learning models.
We’ll start by demystifying what "anomaly" really means - exploring the different types (point, contextual, and collective anomalies) and the difference between unintentional issues and intentional outliers like fraud or abuse.
Then, we’ll look at how anomaly detection is solved in practice: from classical statistical models like ARIMA to deep learning models like LSTM. You’ll learn how ARIMA breaks time series into AutoRegressive, Integrated, and Moving Average components - no math degree required (just a Python library, hehe)! We’ll also uncover why forgetting is a feature, not a bug, when it comes to LSTMs, and how these models learn to detect complex patterns over time.
Throughout, we’ll show how Kafka handles high-throughput streaming data and how Flink enables low-latency, stateful processing to catch issues as they emerge. You’ll leave knowing not just how these systems work, but when to use each type of model depending on your data and goals.
Whether you're monitoring system health, tracking IoT devices, or looking for fraud in transactions, this talk will give you the foundations and tools to detect the unexpected - before it becomes a problem.
(🎤 Interested in speaking at our meetups? Submit talk proposals here.)
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