Senior Software Engineer (Data Engineer) at Textio
Remote, Oregon, USA -
Full Time


Start Date

Immediate

Expiry Date

01 Jun, 25

Salary

170000.0

Posted On

25 Apr, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Systems, Data Warehouse, It

Industry

Information Technology/IT

Description

We support 100% remote work for applicants who reside in the following states: California, Colorado, Hawaii, Illinois, Maryland, Massachusetts, New York, Oregon, Texas and Washington.

EMPLOYEES WANT GROWTH. MANAGERS WANT IT TO BE EASIER. HR WANTS BETTER CONTROL OVER THE PROCESS. TEXTIO HELPS COMPANIES WITH AN INTEGRATED SUITE OF RECRUITING AND FEEDBACK TOOLS THAT HELP ATTRACT THE BEST TALENT POSSIBLE AND DEVELOP TOP PERFORMERS’ SKILLS WITH ACTIONABLE FEEDBACK. AN ORGANIZATION THAT IS SUCCEEDING (AND NOT FAILING) AT FEEDBACK SEE CAREERS SKYROCKET AND BOTTOM-LINE REVENUES SOAR.

At Textio, we’re entering an exciting new phase: exponential usage of our newest product that some of the worlds largest companies are adopting across their entire organization. This growth underscores the need for reliable, high-performance infrastructure to transform data into actionable insights. As a Senior Data Engineer, you’ll be responsible for enhancing and maintaining our existing data warehouse and pipeline infrastructure. You’ll ensure our data systems remain reliable and performant, enabling the team to make timely, data-driven decisions. You’ll also contribute to backend and infrastructure improvements to support our growing analytics needs. This role is ideal for a self-starter who thrives in a fast-paced, ever-evolving startup environment.

Responsibilities
  • Maintain a strong, user-centric approach to feature development
  • Be comfortable working with ambiguous problems and advocating for clear and achievable solutions to them
  • Maintain and enhance our data warehouse to support evolving business needs.
  • Collaborate on a team with diverse functions using the latest AI/LLM technologies
  • Work with teams to enable advanced analytics, including real-time and batch processing.
  • Improve data pipeline scalability and performance to handle increased data volume.
  • Implement monitoring, alerting, and self-healing mechanisms for data systems.
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