Data Engineer

About this role

Who We Are: We build infrastructure that delivers massive amounts of web data to the companies training the world’s most powerful AI models. We're the team that helps to power and support Grass, a bandwidth-sharing network that lets us operate a massive distributed crawler, giving us unique access to high-quality public web data at global scale. On top of that, we’ve built pipelines for ingesting, segmenting, and annotating billions of videos, transcripts, and audio files, powering dataset creation for frontier labs.

We’re lean, technical, and move fast. No red tape, no slow decision-making; just a team of builders pushing to expand what’s possible for open web data and AI. The Role: We are seeking a Data Engineer to support and improve large-scale data pipelines and infrastructure. You’ll work across data collection, processing, transformation, validation, and delivery, with a focus on scalability, reliability, and performance. This is a hands-on role where you’ll work with distributed systems, large datasets, web scraping infrastructure, and production data workloads.

Please note: This role requires a work schedule that overlaps sufficiently with EST business hours to collaborate effectively with the team. Who You Are: - Bachelor’s degree or equivalent work experience - Python (advanced) — strong grasp of async programming, multiprocessing, and writing production-grade code for long-running data jobs - Web scraping at scale — hands-on experience with high-volume scraping (proxies, rate limiting, anti-bot evasion).

Experience with platform APIs and large media/metadata datasets (video platforms, social media) - Distributed data pipelines — experience designing and operating pipelines across many workers/servers using task queues (Celery, Kafka, RabbitMQ, or similar) - Data warehousing — practical experience with columnar/analytical warehouses; Databend, ClickHouse, or BigQuery strongly preferred; comfortable with complex analytical queries, partitioning strategies, cost-aware querying on cloud warehouses - Docker & Kubernetes — containerizing workloads, writing Helm charts/manifests, managing deployments, autoscaling scraping/processing workloads - Linux & bare-metal ops — comfortable managing services on Linux servers, debugging performance issues (disk I/O, network, memory) without managed-cloud abstractions - CI/CD for data workflows (GitHub Actions, ArgoCD) - Writing Scalable API What You'll Be Doing: - Maintain, optimize, and troubleshoot database queries and related data systems to support efficient data access, processing, and reliability.