Guides, tutorials, and insights on job data, APIs, and building with AI.
17 posts
We measured every engineering listing in our index. Across seven roles, 48-62% were posted more than 30 days ago. Here's what that means for your applications — and how to filter them out.
Auto-apply tools are easy to spot and increasingly a disqualifier. The durable advantage is using AI for research and targeting — the work that was too expensive to do by hand.
Connect JobDataLake to Claude in about a minute and ask for real jobs in plain English — 1M+ live listings with salary, skills, and location. No code required.
Building and maintaining scrapers for Greenhouse, Lever, Workday, and Ashby is painful and expensive. Here's why a job data API is the better path — and how to migrate.
How growth and sales teams use a hiring signals API to identify in-market buyers, trigger outbound sequences, and enrich CRM accounts — with code examples for Salesforce and HubSpot.
How to connect Claude to live job data using the JobDataLake MCP server — setup, available tools, example queries, and building custom job-search agents.
A technical deep-dive into how JobDataLake deduplicates job listings at scale — covering syndication chain analysis, ATS ID extraction, fuzzy matching, and the tradeoffs at each layer.
A technical comparison of the top job data APIs in 2026—Coresignal, TheirStack, Fantastic.jobs, JSearch, Bright Data, and JobDataLake.
A practical guide to using job data APIs to backfill a niche job board — covering data feeds, freshness strategy, deduplication, and real code examples.
Learn how to build an AI career assistant using structured job data — covering RAG pipelines, vector embeddings, skill matching, and salary comparison.
A practical guide to the tech stack for building a job board in 2026 — covering frontend, backend, search, data layer, and hosting choices.
A practical guide to building a niche job board that ranks on Google — covering niche selection, SEO architecture, Google for Jobs, content strategy, and monetization.
How B2B sales teams use job posting data as buying signals — identifying company growth, tech stack adoption, and budget signals from hiring patterns.
How to parse job descriptions to extract technology signals, normalize tech names, and build accurate firmographic data from job posting analysis.
The hidden data quality problems in job listing aggregation — syndication chains, deduplication strategies, freshness metrics, and how to build for quality.
Everything developers need to implement Google for Jobs schema markup — JSON-LD JobPosting schema, required fields, enriched data, and validation strategies.
A technical guide to job data enrichment — salary normalization, skill extraction, seniority inference, company enrichment, and building a complete job data model.