This data feed provides access to the last 3 months of job postings from Brazil (BR). On average, we add 3.0K new job postings daily. Old files with job postings are removed after 100 days.
Utilize this data to gain actionable insights into companies, markets, services, or technologies, or to backfill a job board. Identify company signals, analyze hiring trends, spot emerging technologies, and discover potential leads to stay ahead of the competition.
This job postings data feed provides the last 3 months of job postings for Brazil (BR) and is updated daily. With a population of 214.3M, Brazil generates a GDP per capita of 7.5K USD. At the time of writing (June 2024), we add 3.0K new job postings daily, or 91.4K per month.
Our data feed is updated daily with a new exported file containing the newest job postings, while old files are removed after a 100-day (3 months) window. New job postings, aggregated from multiple sources, are added with a two-day delay to accommodate job listings relayed through API pipelines. For example, job listings from January 1st are made available on January 3rd.
We offer the job postings in JSON-L files, which are compressed (gzipped) for easier storage and faster downloads. Each file is named accordingly (e.g., techmap_jobs_br_2024-06-22.jsonl.gz) and contains the job ads for that specific day, with each job ad in a distinct JSON line.
To provide a broader perspective on the volume and trends of job postings, monthly statistics of job counts are readily accessible. Please visit our Data Explorer at https://jobdatafeeds.com/data/countries/br for a detailed overview of these figures.
Use Cases
Job posting data can be used in many ways to extract actionable insights, for example, to boost your sales, marketing, investment, recruitment, business, or competitive intelligence!
In Sales Intelligence job postings can help to generate new leads faster, easily find lookalikes of your customers, or enhance successful lead conversions.
Competitive Intelligence is improved by identifying competitors faster or easily analyzing competitor's offerings. Additionally, job data can be leveraged to discern trends in their areas of expansion or contraction.
For Market Intelligence job data can help to identify market trends faster, easily extract market insights, or support data-driven marketing decisions.
In Recruitment Intelligence job data helps to identify hiring trends, market salaries, talent pools, and more.
Furthermore, they can be used to backfill a profession-, language-, or workplace-specific Job Board.
Please note that we add job data with a two-day delay to accommodate job ads relayed through API pipelines (i.e. job postings from January the 1st are provided in the course of January the 3rd).
Pricing Information
This data set is available as a monthly subscription and includes the data files for job postings for at least the last 3 months (100 days) and is updated with new job postings daily. The data is for a customer's individual use and may not be resold.
Additional Information
If you're interested in bigger bundles (e.g., EU, Worldwide, DACH, BeNeLux, etc.) or other countries, not yet listed in AWS Data Exchange, please visit our job data product website or contact data@techmap.io. We can add data feeds for other countries in a few days.
If you have questions about our data products, please contact data@techmap.io
About Us
Techmap developed and operates Techmap.io, a powerful global workplace search engine designed for developers, students, and freelancers to find companies that use specific technologies. To operate Techmap, we crawl the Internet daily and collect millions of international job postings. We extract information on the companies from their job ads to identify technologies and tools in use.
But we are not the only ones who find these job postings valuable, which is the reason we sell the same job listings we use to extract information on companies. See our data product website (jobdatafeeds.com) for more information.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
This listing uses a single pricing dimension: Product Access (Units), which grants subscribers access to the product. You subscribe through a contract covering job posting data for Brazil over a rolling 3-month window. The data arrives as daily files delivered via AWS S3, updated regularly. Because there is one dimension, pricing does not scale across tiers or instance sizes. You receive access to the complete Brazil job posting feed for the contract term. Billing is consolidated into your AWS account, so no separate vendor contract or credentials are needed.
Top-of-mind questions for buyers
What does one unit of Product Access grant me for Brazil?
One subscription unit grants access to the complete Brazil job posting feed. You receive every posting collected for Brazil, with no cap on volume. Data covers a rolling 3-month window, delivered as daily gzip-compressed JSON Lines files, one file per day, retrievable from an S3 bucket in your own AWS account.
Does my cost change as Brazil's job posting volume grows or shrinks over time?
No. Product Access covers the full Brazil feed at a fixed contract rate. You receive everything collected, regardless of monthly volume, with no per-posting metering and no overage charges. Whether Brazil produces more or fewer postings in a given month, your subscription cost stays the same.
What format do the Brazil data files use, and are other formats included?
Files arrive exclusively as gzip-compressed JSON Lines. Each line is one complete job posting in JSON. CSV, XML, Parquet, RSS, and Atom are not natively included. You decompress the files and process them in your own pipeline. Sample conversion code is available on request from the vendor.
Tell us how we can improve this page, or report an issue with this product.
Give us feedbackReport a problem with this product or seller
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA).
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
This data feed provides access to the last 3 months of job postings from the MERCOSUR region. On average, we add 42k new job postings daily. Old files with job postings are removed after 100 days.
Utilize this data to gain actionable insights into companies, markets, services, or technologies, or to backfill a job board. Identify company signals, analyze hiring trends, spot emerging technologies, and discover potential leads to stay ahead of the competition.
TabPFN-3-Plus by Prior Labs is the latest generation Tabular Foundation Model. A single forward pass tops the public TabArena benchmark for classification and regression, scales to 1M training rows (at 200 features) or 100k rows (at 2000 features), and runs up to 20x faster than TabPFN-2.5. TabPFN-3-Plus also natively handles text features: string-valued columns are accepted directly, without requiring upstream featurization, and are encoded jointly with numeric and categorical features inside the model. Released under the TABPFN-3.0 License v1.0 for research and internal evaluation.
Llama 3.3 Nemotron Super 49B V1.5 is a significantly upgraded version of Llama 3.3 Nemotron Super 49B V1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens.