1from apify_client import ApifyClient
2
3
4
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7
8run_input = {
9 "items": [
10 {
11 "companyName": "Northwind Analytics",
12 "title": "Senior Data Engineer",
13 "location": "Austin, TX",
14 "postedAt": "2026-10-20",
15 "description": "Northwind Analytics is building a new cloud data platform from the ground up. You will migrate our legacy Oracle warehouse to Snowflake, build ELT pipelines in dbt and Airflow, and set up data quality monitoring. This is one of the first hires on our new data platform team. Experience with Python, SQL and AWS required.",
16 },
17 {
18 "companyName": "Northwind Analytics",
19 "title": "Analytics Engineering Manager",
20 "location": "Remote, US",
21 "postedAt": "2026-10-24",
22 "description": "We are scaling our analytics engineering function from 2 to 8 people over the next year. You will own our dbt project, define data contracts and roll out Looker as our company-wide BI tool. Our stack: Snowflake, dbt, Fivetran, Looker.",
23 },
24 {
25 "companyName": "Bluefin Logistics",
26 "title": "Salesforce Administrator",
27 "location": "Chicago, IL",
28 "postedAt": "2026-10-18",
29 "description": "Bluefin Logistics is implementing Salesforce Sales Cloud for our 120-person sales team. As our first Salesforce Administrator you will configure objects, build reports and dashboards, and integrate Salesforce with NetSuite and HubSpot.",
30 },
31 ],
32 "icpDescription": "We sell a data observability platform that catches broken pipelines and bad data before it reaches dashboards. Best fit: B2B companies with 200-5,000 employees building or scaling a modern data stack (Snowflake, BigQuery, Databricks, dbt, Airflow). Buyers: Head of Data, data platform and analytics engineering teams. Strong signals: new data platform team, warehouse migration, data quality or data contracts initiatives.",
33}
34
35
36run = client.actor("seasons-ai/jobs-to-hiring-signals").call(run_input=run_input)
37
38
39print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
40for item in client.dataset(run.default_dataset_id).iterate_items():
41 print(item)
42
43
