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A group for experienced and aspiring data professionals.
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Upcoming events
6

From APIs to Warehouses: AI-Assisted Data Ingestion with dlt
·OnlineOnlineThis hands-on workshop focuses on building reliable data ingestion pipelines to data warehouses (for example, Snowflake) using dlt (data load tool), enhanced with LLMs, the dlt dashboard, and dlt MCP.
You’ll work through the key building blocks of a production-ready ingestion setup, including:
- Extracting data from APIs, files, and databases
- Normalizing data into consistent schemas
- Writing data to a data warehouse (e.g. Snowflake)
- Using LLMs to accelerate dlt pipeline development
- Validating data and schema changes using the dlt dashboard and dlt MCP
The session is fully practical and code-driven. By the end of the workshop, you’ll understand how to design maintainable, scalable ingestion pipelines and use AI and validation tools to build them faster and with confidence.## About the Speaker
Aashish Nair is a Data Engineer at dltHub and the creator of the famous dlt deployment course, where he teaches best practices for running dlt pipelines in production.
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This event is sponsored by dltHub
67 attendees![GenAI transforming Engineering: Agents and Guardrails [IN-PERSON!]](https://secure.meetupstatic.com/photos/event/b/7/1/c/highres_532246876.jpeg)
GenAI transforming Engineering: Agents and Guardrails [IN-PERSON!]
Zalando BHW, Hedwig-Wachenheim-Straße 7, 10243 Berlin, Germany, Berlin, DEMeet us for in-person talks at Zalando on February 17, 2026!
Join us for an evening of practical talks on how GenAI is reshaping modern engineering workflows, from code agents to production-grade, guardrailed systems.
Please make sure you register with your full name, as it will be checked by security on arrival.
📅Date and Time:
Tuesday, February 17, 2026
Doors open: 18:00
Opening & announcements: 18:30
Talks start: 18:35
Wrap-up & networking: ~20:30📍Location: Zalando Office BHW
Berlin Hedwig-Wachenheimstraße 7 (BHW)
The main entrance is on the west-side of the building (towards Uber Arena). Participants should register at the Helpdesk, and will be picked up from there.📝 Agenda:
18:00 – Doors Open Pizza, drinks, and networking
18:30 – Opening Short welcome and announcements from the organizers (5 min)### 18:35 – Talk #1
Building Blocks of Modern Code Agents: Reusable Skills and Role-Based Subagents
Alexey Grigorev — DataTalk.Club
A practical map of modern code agent types and the two key building blocks that make them reliable in real-world development workflows.
Code agents appear in many forms today: chat-based assistants, cloud agents running CI-like workflows, and IDE or terminal agents embedded directly into development environments. In this talk, we’ll map these main categories, discuss what each is good at, and highlight where they commonly fail.We’ll then zoom in on two core building blocks used in modern agent tooling:
- Skills / playbooks — reusable, step-by-step workflows (e.g. understand the repo → implement a change → run checks → produce a clean diff)
- Subagents — specialized roles such as planner, implementer, reviewer, and tester that split complex work into focused, reliable steps
Format: 20 min talk + 10 min Q&A
### 19:05 – Talk #2
Guardrailed Agents at Scale: Zalando’s Support Agent for Incident Triage and Stakeholder Q&A
Ivan Potapov, Saugandh Karan — Zalando SE
We’ll share how Zalando built and shipped a specialized internal support agent that helps engineering teams answer stakeholder questions and triage operational alerts — without drowning in context or compromising production safety.
The agent enriches user requests with relevant observability context (metrics, logs, traces, recent deploys), summarizes what matters, and produces a severity assessment with recommended next steps.
A key challenge is context explosion: pulling “all the data” quickly becomes noisy, slow, and risky. We’ll walk through the architecture patterns and guardrails that keep the system production-ready:
- Retrieval and ranking strategies
- Strict tool boundaries
- Policy-driven response formats
- Evaluation checks to prevent overconfident or unsafe guidance
Finally, we’ll cover rollout and migration: introducing the agent alongside existing workflows, aligning it with architecture guidelines, and iterating based on real incident feedback.
Format: 20 min talk + 10 min Q&A
### 19:35 – Pizza, Drinks & Networking
Invite your friends and join our meetup. Special thanks to our hosts Zalando.
198 attendees
Context Engineering for Agentic Hybrid Applications
·OnlineOnlineResearch survey and upcoming trends discussion - Ivan Potapov and Tobias Lindenbauer
As an agent keeps running, its context window balloons with tool logs, stale diffs, and repeated data dumps. The model starts drowning in irrelevant details and falls victim to "lost-in-the-middle" effects — missing critical facts buried deep in oversized prompts.We'll walk through research for keeping only high-signal observations: masking vs. summarization trade-offs, compressing bulky tool output (drawing from ideas like LLMLingua-2), and pruning dead branches from the agent's trajectory so it stops dragging noise forward. We'll also share insights on cutting LLM call costs along the way.
Then we'll connect those techniques to bigger-picture design: memory hierarchies (session → working set → notes → cross-session) and standardized tool interfaces like MCP that reduce "context debt" and keep the agent's working set clean.
Finally, we'll look at where the field is heading — toward a world where Context Engineering becomes something you train, not just script.
About the Speakers:
Tobias Lindenbauer is an AI researcher at JetBrains Research, where he advances efficient and effective code agents that robustly solve long-horizon software engineering tasks. Currently, he is most interested in efficiency topics, context management, interpretability and data synthesis. He recently presented “The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management” at the Deep Learning for Code workshop at NeurIPS 2025, highlighting practical pitfalls of LLM summarization-based context strategies and evidence for more computationally efficient alternatives.
Ivan Potapov is a Research Engineer in Discovery Search & Ranking at Zalando, where he builds search retrieval and ranking systems. He teaches data engineering, AI agents, and LLM alignment, with a focus on bridging software engineering and applied ML. His recent work centers on long-running agents and context engineering—memory, state, and retrieval—exploring why many code-first agent designs fall short. His key thesis: context management is becoming something we train and iterate on, not just script. https://blog.ivan.digital/context-engineering-for-agentic-hybrid-applications-why-code-agents-fail-and-how-to-fix-them-076cab699262
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25 attendees
Past events
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