**Glassdoor** is an American website where current and former employees anonymously review companies, operated by the company of the same name.[[1]](https://en.wikipedia.org/wiki/Glassdoor#cite_note-1)
In 2018, the company was acquired by the Japanese [Recruit Holdings](https://en.wikipedia.org/wiki/Recruit_(company) "Recruit (company)") (Owner of [Indeed](https://en.wikipedia.org/wiki/Indeed "Indeed")) for US$1.2 billion, and it continues to operate as an independent subsidiary.[[2]](https://en.wikipedia.org/wiki/Glassdoor#cite_note-2)[[3]](https://en.wikipedia.org/wiki/Glassdoor#cite_note-3)
### ๐ About This Actor
Scrape any data about jobs, companies, salaries from Glassdoor.com
#### ๐ Features :
- ๐ซ Search **Jobs**, **Companies**, **Salaries**, **Interviews**, **Conversations**, **Bowls**, etc.
- ๐ซ Scrape **Companies** detail info
- ๐ซ Scrape **Job** detail info
- ๐ซ Filter anything by _Job Title_, _Location_, etc.
- ๐ซ Multiple **QUERIES** in single request
#### ๐ Disclaimer :
- โข๏ธ _Free Trial limited to the first **100** results_
- โข๏ธ _Free Trial limited to the first **5** queries_
- โข๏ธ _This Actor uses minimal **INPUT** but leverages the power of **GDQL**_
- โข๏ธ _Please review the **TUTORIAL** for the best experience._
### ๐ TUTORIAL
#### ๐ Parameters
| Name | Type | Description |
|-|-|-|
| `query` | ***string array*** | Query, Search Terms or URL |
| `limit` | ***integer*** | Number of results (per-query)|
#### ๐ Filters
| Name | Type | Description |
|-|-|-|
| `title` | ***string*** | Job title |
| `location` | ***string*** | Job location |
#### ๐ Basic Usage
Search for jobs
```yaml
{ "query": ["starship engineer"], "filters.location": "Jupiter", "limit": 20 }
````
#### ๐ Advanced Usage
##### GLASSDOOR Query Language (`GDQL`)
```html
[ (aka. Subreddit)* | | **bowls:<*BOWL\_ID*>** | `bowls:62283a4efa19c8002e047127` | ***Bowl Data*** | | `/info` | `bowls:62283a4efa19c8002e047127/info` | Bowl info *(default)* | | `/posts` | `bowls:62283a4efa19c8002e047127/posts` | Bowl posts content | | `/pins` | `bowls:62283a4efa19c8002e047127/pins` | Bowl pinned posts | | `/cards` | `bowls:62283a4efa19c8002e047127/cards` | Bowl cards | | **posts:<*KEYWORD*>** | `posts:python` | ***Search Community Posts**
(conversations)* | | **posts:<*POST\_ID*>** | `posts:671a32f88c8a715bf66d8279` | ***Post Data*** | | `/info` | `posts:671a32f88c8a715bf66d8279/info` | Post info *(default)* | | `/comments` | `posts:671a32f88c8a715bf66d8279/comments` | Post comments | | `/replies` | `posts:671a32f88c8a715bf66d8279/replies` | Post comments replies | | **https:<*URL*>** | `https://www.glassdoor.com/...` | ***Start URL***| | | *... to be continued ...* | | ### ๐ INPUT EXAMPLES See column **Example** above for `QUERY` input eg: ```yaml { "query": ["companies:1651/reviews"] } ``` #### Example #1: Search Jobs Search for job title ```yaml { "query": ["starship engineer"],"limit": 20 } ``` Search for job title in specific location (`filters.location`) ```yaml { "query": ["starship engineer"], "filters.location": "Indonesia", "limit": 20 } ``` Search for companies by job title (`employers:
Redis DBA - R01543166
\nAbout Brillio:
\n
\n
\n
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction.\n
Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.
\nRedis DBA
\nPrimary Skills
- \nDB2, Informix DBA, Middleware, MongoDB, MySQL, Oracle DBA, Oracle PL/SQL, Postgres, SQL DBA\n
Specialization
- \nDatabase & Middleware Support: Engineer, Database & Middleware\n
Job requirements
\nBrillio LLC is a fast growing, pure play Digital Transformation Solutions and Services firm backed by Bain Capital private equity. Founded in 2014 and headquartered in Silicon Valley, Brillio is focused on delivering design led solutions for our customers. We are not an IT Services company trying to pivot and support the “next big thing.” As a digitally native company, everything we have done and will continue to do, will have this laser focus.
\n
\nOur culture is driven by Brillio’s Core Values and it is the foundation of who we are as an organization. We equip and empower our employees to push boundaries with an ‘Founder’s Mindset’ spirit as they craft ‘Customer Success’ stories. With our customer-centric approach, we deliver ‘Excellence and Innovation’ at every level of our collaboration and this is what fuels Brillio’s growth story. While we are proud of our journey so far, ‘ Care and Compassion’ about our customers, employees and the community where we thrive in. We strive to stay committed to lighting up 100,000 young minds through our CSR efforts.
\n
\nRole: Redis DBA
\nYears of Experience: 3+ years
\nLocation: Remote
\n
\nRequired Qualifications:
- 3 years of experience with Redis Opensource
- 3 years of experience with Redis Enterprise – clustering, sharding, replication, high availability, multi-region (active-active) setup
- 3 years of experience with Azure Cache for Redis and or GCP Memory Store
\n
\nCommon Required Qualifications:
- 8 years of combined experience in Database Administration Engineering
- Advanced knowledge of performance tuning, monitoring alerting, observability, dashboarding
- Advanced knowledge of process/query analysis, buffer tuning, client/server connectivity and database consistency checks\n
- Advanced knowledge of maintenance routines, upgrades, patching
- Experience in handling physical/logical backups & restores and data refreshes.
- Experience in Linux/Unix shell commands or Windows PowerShell or Python
- Experience in OS fine tuning with regards to Network I/O, Disk
- Should be able to collaborate with DBA peers and mentor, work well in a team and independently
- Should be able to multi-task i.e. parallelize multiple environment build data migration
- Should be able to work off-hours for application release support, Linux patching support, maintenance windows
- Experienced with security and data compliance aspects of database
- Well versed with latest version of database
\n
\nPreferred qualifications
- Experience with automation via Ansible, Terraform
- Experience with deployments (CI/CD) with GHA, GitHub, Jenkins\n
\n
Why should you apply for this role?
\nAs Brillio continues to gain momentum as a trusted partner for our clients in their digital transformation journey, we strive to set new benchmarks for speed and value creation. The Advisory and Value Assurance (AVA) team at Brillio is at the forefront of leading this charge by reimagining and executing how we structure, sell and deliver our services to better serve our clients.
\n
\nKnow more about <PPE practice specific page link from Brillio website>
\nPPE: https://www.brillio.com/platform-and-product-engineering/
\n
\nKnow what it’s like to work and grow at Brillio: https://www.brillio.com/join-us/
\n
\nEqual Employment Opportunity Declaration
\nBrillio is an equal opportunity employer to all, regardless of age, ancestry, colour, disability (mental and physical), exercising the right to family care and medical leave, gender, gender expression, gender identity, genetic information, marital status, medical condition, military or veteran status, national origin, political affiliation, race, religious creed, sex (includes pregnancy, childbirth, breastfeeding, and related medical conditions), and sexual orientation.
\n
- \n#LI-AS1
\n
\nKnow what it’s like to work and grow at Brillio: Click here
\n\nSalary: 80,000-100,000 USD per year salary
# Actor input Schema
## `query` (type: `array`):
๐ก Job Title
## `mode` (type: `string`):
๐ก This is shortcuts for QUERY commands
## `limit` (type: `integer`):
๐ก Number of results per QUERY
## `filters.location` (type: `string`):
๐ก Location Name or ID
## `filters.easy_apply` (type: `boolean`):
No description
## `filters.remote` (type: `boolean`):
No description
## `filters.min_salary` (type: `integer`):
No description
## `filters.max_salary` (type: `integer`):
No description
## `filters.company` (type: `string`):
๐ก Company Name or ID
## `filters.min_rating` (type: `string`):
๐ก Company minimal rating
## `filters.company_size` (type: `string`):
๐ก Number of employees
## `filters.job_age` (type: `integer`):
๐ก Job posted date
## `filters.seniority` (type: `string`):
No description
## `filters.topic` (type: `string`):
No description
## `filters.employment_status` (type: `string`):
No description
## `filters.language` (type: `string`):
๐ก Reviews Language
## `filters.industries` (type: `array`):
No description
## `filters.functions` (type: `array`):
No description
## `dev_proxy_config` (type: `object`):
๐ก Supported protocol:HTTP(S), SOCKS5
{http|socks5}://{user:pass}@{hostname|ip-address}:portExample:
socks5://example.com:9000
## `dev_custom_headers` (type: `array`):
๐ก Additional HTTP Headers
## `dev_custom_cookies` (type: `array`):
๐ก Additional HTTP Cookies
## `dev_transform_fields` (type: `array`):
๐ก Transform the resulting output. Select only needed fields.For nested object use DOT. For example:
address.streetAddress
For nested array use NUMBER (index of array element starting from index=0). For example:
images.0.url## `dev_dataset_name` (type: `string`): ๐ก Save results into custom named Dataset, use mask to customize dataset name
{ACTOR} = actor name
{DATE} = date (YYYYMMDD)
{TIME} = time (HHMMSS)This masks can be used to autogenerate Dataset Name.
example:
data-{DATE}Depending on today date the dataset name will be:
data-20230603default:
data-{ACTOR}-{DATE}-{TIME}
## `dev_dataset_clear` (type: `boolean`):
Clear Dataset before insert/update.
## `dev_no_strip` (type: `boolean`):
๐ก Keep/Save empty values (NULL, FALSE, empty ARRAY, empty OBJECT, empty STRING)
## Actor input object example
```json
{
"query": [
"UFO reverse engineer"
],
"limit": 5
}
```
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the 'HTTP(S), SOCKS5
{http|socks5}://{user:pass}@{hostname|ip-address}:portExample:
socks5://example.com:9000"
},
"dev_custom_headers": {
"title": "๐ HTTP HEADERS",
"type": "array",
"description": "๐ก Additional HTTP Headers",
"items": {
"type": "object",
"required": [
"key",
"value"
],
"properties": {
"key": {
"type": "string",
"title": "Key"
},
"value": {
"type": "string",
"title": "Value"
}
}
}
},
"dev_custom_cookies": {
"title": "๐ฐ HTTP COOKIES",
"type": "array",
"description": "๐ก Additional HTTP Cookies",
"items": {
"type": "object",
"required": [
"key",
"value"
],
"properties": {
"key": {
"type": "string",
"title": "Key"
},
"value": {
"type": "string",
"title": "Value"
}
}
}
},
"dev_transform_fields": {
"title": "โป๏ธ CUSTOM FIELD",
"type": "array",
"description": "๐ก Transform the resulting output. Select only needed fields.For nested object use DOT. For example:
address.streetAddress
For nested array use NUMBER (index of array element starting from index=0). For example:
images.0.url", "items": { "type": "object", "required": [ "key", "value" ], "properties": { "key": { "type": "string", "title": "Key" }, "value": { "type": "string", "title": "Value" } } } }, "dev_dataset_name": { "title": "๐ CUSTOM STORAGE", "type": "string", "description": "๐ก Save results into custom named Dataset, use mask to customize dataset name
{ACTOR} = actor name
{DATE} = date (YYYYMMDD)
{TIME} = time (HHMMSS)This masks can be used to autogenerate Dataset Name.
example:
data-{DATE}Depending on today date the dataset name will be:
data-20230603default:
data-{ACTOR}-{DATE}-{TIME}"
},
"dev_dataset_clear": {
"title": "Clear Storage",
"type": "boolean",
"description": "Clear Dataset before insert/update."
},
"dev_no_strip": {
"title": "Disable data cleansing",
"type": "boolean",
"description": "๐ก Keep/Save empty values (NULL, FALSE, empty ARRAY, empty OBJECT, empty STRING)"
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```