Point your AI agent at freehire and let it find you a job. A CLI and an MCP server over the whole job API — no browser.
Manages accounts receivable, billing, and collections for a global SaaS company, handling invoicing, deal closures, and customer renewals using ERP tools and spreadsheets.
Presales Solutions Architect at Workato designs and demonstrates integration solutions for enterprise customers, focusing on iPaaS and automation technologies to align with business needs.
About Workato Workato is the leading Control and Execution Platform for Enterprise AI — the neutral platform enterprises trust to put AI to work across their business. Workato unifies data, applications, and processes…
Own the roadmap for Workato’s Enterprise MCP gateway, defining authentication, policy enforcement, and observability to safely scale AI agents across enterprise systems.
Senior Solutions Consultant at Workato designs and demonstrates iPaaS integrations, advises enterprise customers on automation and AI agent platforms, and drives technical sales through workshops and POCs.
A 6-month contract covering an employee's parental leave: the contractor independently runs Workato's Salesforce environment — handling user access, security, configuration, production incidents, and releases — while protecting revenue-critical processes across Sales Cloud, Salesforce CPQ, Certinia, and Vivun.
As a Data Engineer Intern you’ll design and implement data pipelines that load data into Snowflake, build efficient data models, and optimize analytical workflows with dbt Cloud, while supporting CI/CD processes and AI initiatives.
Workato is hiring a PostgreSQL DBA in Lisbon or Porto to own day-to-day operations of its Amazon Aurora PostgreSQL fleet: schema/index design on multi-terabyte tables, query tuning, PgBouncer pooling, upgrades and migrations, plus automating runbooks with Terraform/Ansible and sharing a developer-support duty rotation.
Analytics Engineer on Workato's Product Management team in Singapore, owning end-to-end delivery of mission-critical data products: designing and scaling dbt data models, ensuring data quality and reliability, and turning product usage data into insights for stakeholders. Core stack: SQL, dbt, cloud data warehouses (Snowflake/BigQuery), Python, and orchestrators like Airflow/Dagster.
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