Data Engineer

Wynd Labs

5d•Remote

About The Position

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.

Requirements

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

Responsibilities

Maintain, optimize, and troubleshoot database queries and related data systems to support efficient data access, processing, and reliability.
Assist in creating, maintaining, and improving data pipelines used to collect, process, transform, validate, and deliver large-scale datasets.
Support web scraping and data collection initiatives, including developing, testing, and maintaining scripts or tools used to gather publicly available data in accordance with Company requirements.
Monitor and troubleshoot data pipeline issues, identify data quality concerns, and assist in implementing timely fixes to maintain data accuracy and operational continuity.
Document engineering work, including database queries, pipeline processes, scraping workflows, technical decisions, issues encountered, and resolutions implemented.
Participate in research and development projects to improve the Company’s data products and workflows.