Full-Time
Confirmed live in the last 24 hours
Automated data movement and management platform
$202.5k - $253.1k/yr
Senior
Oakland, CA, USA
Hybrid position requiring two days in the office each week.
Fivetran provides an automated platform for data movement, helping businesses manage their data efficiently. The platform uses the ELT (Extract, Load, Transform) process to take data from various sources, organize it, and convert it into a format suitable for analysis or operational use. This automation is particularly useful for data engineers, allowing them to focus on more significant projects while ensuring reliable data pipelines. Fivetran offers over 400 pre-built, no-code connectors for easy integration with different data sources, and it automatically manages updates, data normalization, and schema drift. The company emphasizes security with customizable features that comply with data protection regulations like GDPR and HIPAA. Operating in the cloud-based data management market, Fivetran likely uses a subscription or usage-based pricing model, aiming to simplify data handling for businesses of all sizes.
Company Size
1,001-5,000
Company Stage
Debt Financing
Total Funding
$852.5M
Headquarters
Oakland, California
Founded
2012
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Health Insurance
Unlimited Paid Time Off
Stock Options
Professional Development Budget
Phone/Internet Stipend
Mental Health Support
The following is a guest post and opinion of Zac Cheah, Co-Founder of Pundi AI.The brouhaha over autonomous artificial intelligence (AI) agents taking up jobs and radically transforming industries like healthcare and finance requires close inspection. Autonomy is a spectrum, where even the most autonomous AI agents need some form of human intervention to work appropriately.Fully autonomous AI agents are impossible. And rather than eating up jobs, autonomous AI agents create new work opportunities where humans assist AI agents’ functions throughout their lifecycle.Diversifying Job Options Within the AI IndustryAll autonomous AI agents in production or deployment stages require human action because they cannot operate independently, thereby creating job openings. Although AI agents operating at scale are beyond a single person’s cognitive capacities, each agent has multiple human-led teams in the development pipeline.These agents need human developers to build the underlying infrastructure, code the algorithm, prepare human-labeled datasets for training, and oversee auditing procedures.For example, an autonomous AI agent’s accuracy depends on high-quality data training and performing repeated analytical tests. No wonder 67% of data engineers spend hours preparing datasets for AI model training.Since fragmented datasets lead to operational problems for autonomous agents, project teams have to clean data before training. Moreover, as data gaps can generate wrong output, developers must ensure an AI agent’s integrity and market positioning through rigorous evaluation
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