Two Six Technologies is seeking a highly skilled Data Collection Engineer to design, scale, and maintain our distributed web scraping and data extraction infrastructure. In this role, you will be responsible for building resilient data pipelines that harvest data from complex web ecosystems, ensuring strict data quality through automated validation, and managing containerized workloads at scale. If you thrive on reverse-engineering web applications, overcoming anti-bot barriers, and orchestrating distributed systems, we want you on our team.

Location: 100% Remote

What you will Do:

• Distributed Crawler Development: Design and deploy high-performance, distributed web scrapers using Python and Scrapy to extract massive datasets efficiently.
• Dynamic Content Extraction: Utilize Browser Scripting tools to navigate, interact with, and extract data from modern, dynamic, and JavaScript-heavy websites.
• Infrastructure & Container Orchestration: Deploy, scale, and manage scraping workloads on Kubernetes, ensuring optimal resource allocation and fault tolerance.
• Data Validation & Quality Assurance: Define strict JSON Schemas and leverage Pydantic to enforce data types, validate incoming payloads, and catch data drift early.
• Data Ingestion & Storage: Build and optimize search and storage pipelines using Elasticsearch, transforming raw web dumps into highly structured, searchable data.
• Pipeline Workflow Management: Architect robust pipeline workflows to manage the end-to-end data lifecycle—from discovery and extraction to validation and storage.
• Anti-Bot & Proxy Engineering: Manage complex proxy rotation, session handling, and browser fingerprinting to maintain high success rates against advanced anti-scraping systems.

What you will need (basic qualifications):

• Experience: 7+ years of professional software engineering experience, with a heavy focus on web scraping, data engineering, or distributed systems.
• Analytical Mindset: Excellent reverse-engineering skills, with the ability to dissect network traffic, unearth hidden APIs, and bypass complex web barriers.
• Reliability Focus: A strong commitment to data integrity, system monitoring, and building self-healing scraping systems.
• Core Language: Expert-level proficiency in Python.
• Scraping Frameworks: Deep experience with Scrapy and distributed scraping architectures (e.g., handling distributed queues, broad vs. deep crawling).
• Automation & Browser Scripting: Proven experience with browser automation tools (Playwright, Selenium, or Puppeteer).
• Data Serialization & Validation: Mastery of JSON, JSON Schema, and data validation using Pydantic.
• Search & Analytics Engines: Hands-on experience indexing, querying, and optimizing Elasticsearch clusters.
• Orchestration: Strong proficiency in managing and scaling applications within Kubernetes environments.
• Workflow Management: Experience building structured pipeline workflows to handle complex, multi-stage data extraction tasks.
• Education: Bachelor’s degree in Computer Science, Engineering

Nice if you Have:

• AI & Intelligent Extraction: Experience leveraging LLMs or Computer Vision for adaptive scraping, parsing unstructured HTML, or bypassing CAPTCHAs (AI in data collection).
• Cloud Infrastructure: Strong hands-on experience with AWS ecosystems (e.g., EKS, EC2, S3, RDS).
• Relational Databases: Proficiency in SQL for querying, schema design, and storing structured relational data.
• In-Memory Data Structures: Experience with Redis (specifically for caching, deduplication, or as a Scrapy distributed queue back-end).
• Event Streaming: Familiarity with Apache Kafka for real-time data streaming and decoupled pipeline architectures.
• Containerization: Strong foundation in Docker for local development and containerizing scraping microservices.
• DevOps: Experience with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) for automated testing and deployment of crawlers.

Clearance Requirement:

• Eligible to obtain a clearance