Staff Software Engineer - Ingestion

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

Median wage (BLS OEWS)

$116,543 national median

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Staff Software Engineer - Ingestion Bengaluru, India P-1403 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems - from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Ingesting data into the Lakehouse is a strategic area of investment for Databricks and a key enabler for Data and AI workflows. Lakeflow Connect is looking to solve this problem by providing ready-to-use, point-and-click connectors for a wide variety of sources, including enterprise applications (like Salesforce, Workday, ServiceNow, SharePoint), databases (e.g., SQL Server), cloud storage, message queues, and local files. In addition to being an important part of Lakeflow and Data Engineering, Connect is also a key platform capability. Every surface in Databricks (Dashboards, Notebooks, SQL, AI) requires ingestion capabilities and the lead for this role will need to work closely with other products to embed Connect into these surfaces. We are looking for engineers with experience in core Database internals to join our Lakeflow Connect team. A key part of Connect is to extract data from OLTP systems while imposing minimal load on production systems. To do this efficiently we are building systems that use techniques such as incremental data capture, log parsing, etc. We are looking for engineers who continue to be hands on and are looking to make a large

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