Acryl Data

HQ
Mountain View, California, USA
33 Total Employees
Year Founded: 2021

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Jobs at Acryl Data
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Recently posted jobs

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Palo Alto, CA, USA
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Database
Lead the Site Reliability Engineering team to enhance platform reliability and performance, ensure operational excellence, and drive multi-cloud deployment initiatives for DataHub Cloud.
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Bengaluru, Bengaluru Urban, Karnataka, IND
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Database
Enhance ingestion framework for data and metadata, build connectors, run in cloud, lead team of junior engineers, and leverage AI capabilities.
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36 Locations
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The Software Engineer will provide technical support to customers, collaborate with engineering teams, and drive customer success initiatives while leveraging Python and SQL expertise.
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East Coast, NZL
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Database
The Sales Engineer will support the sales team by providing technical expertise and building relationships with customers, demonstrating software, and ensuring smooth transitions between sales and customer success.
21 Days AgoSaved
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In-Office
Bengaluru, Bengaluru Urban, Karnataka, IND
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Database
Design, build, and optimize scalable batch and streaming data systems; ensure data quality, observability, and governance; integrate metadata and lineage; troubleshoot production issues; collaborate across teams and mentor junior engineers.
22 Days AgoSaved
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Bengaluru, Bengaluru Urban, Karnataka, IND
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Database
The role involves building SaaS platform capabilities, driving enhancements in data observability and governance, leading junior engineers, and working collaboratively with stakeholders.
22 Days AgoSaved
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Bengaluru, Bengaluru Urban, Karnataka, IND
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Database
Develop core capabilities for a SaaS platform, enhancing Data Discovery, Observability, and Governance while ensuring performance and scalability.
22 Days AgoSaved
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In-Office
Palo Alto, CA, USA
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Database
Lead development of DataHub's metadata platform, building scalable systems, APIs, and architectures for metadata collection and processing.
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In-Office
Palo Alto, CA, USA
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Database
Lead development of DataHub's ingestion framework, building scalable, fault-tolerant ingestion systems, clean connector APIs, event-driven real-time metadata processing, schema mapping, and versioning for AI assets at enterprise scale.