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Design, build, and optimize high-volume, high-performance ELT pipelines for a centralized data warehouse

Build and maintain scalable, distributed, fault-tolerant data pipelines on GCP, including BigQuery-based lakehouse layers and Dataproc-driven Delta Lake workflows

Build and maintain scalable, distributed, fault-tolerant data pipelines on GCP

Over 6 years of experience as a Data Engineer

obsess with crafting code that is fast, reliable and practical for the problem

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Design, build, and operate core components of our lakehouse platform, including Apache Iceberg table management (data compaction, data layout optimization, materialized view scheduling…) and Iceberg catalog

Act as a subject matter expert for data observability on behalf of the Datadog advocacy and engineering teams

Work across the entire Linux stack, from kernel, networking, storage, to applications,

You have a passion for designing, implementing, and operating stable, scalable, and efficient data systems

AI-Augmented Intelligence: In close partnership with functional leaders, architect and deploy agent-assisted intelligence systems that move beyond static dashboards. Build workflows that proactively reason across silos (e.g.

Develop a clear, technical understanding of Prefect's platform — including durable execution, AI agent orchestration, MCP, and the broader ecosystem of competing solutions

Semantic Layer Architecture: Build and maintain high-fidelity dbt and SQL models that serve as the "ground truth" for our complex, usage-based revenue models.

Build the systems and pipelines that enable Search, Product, and other teams to independently access and utilize reliable eval verdicts without bottlenecks

Automation & AI: Extend existing automated workflows using Python + SQL and AI tools (Cursor, Claude Code, LangChain/LangGraph, etc.); assist in building scalable digital-first finance processes.

Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation

Develop and execute comprehensive event strategies across all Prefect products, including program mix, calendar planning, and audience targeting

Design for Action: Build AI integrations that do more than just summarize text. Design systems that can securely read from and write back to our core platforms (e.g.

Accountability for the design and development of an internal product