Strategic Post-Sales Data Solutions Architect

Fivetran

Denver (CO)

Hybride

USD 155 000 - 175 000

Plein temps

Il y a 9 jours

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Avantages offerts par ce poste

Medical insurance
Paid time off
RSU stock grants
Professional development
Cell phone stipend
Mental health support

Résumé du poste

Fivetran is seeking a Customer Solutions Architect in Denver for a hybrid, full-time role. You will own the technical narrative post-go-live, driving adoption, expansion, and platform value for a portfolio of accounts.

You’ll partner with AEs, SEs, and customers to align technical outcomes with business goals. Responsibilities include leading architecture discussions, delivering workshops, and guiding customers through data pipeline best practices.

Qualifications

  • 2+ years in a post-sales technical role.
  • Own post-sales outcomes including adoption, renewal and expansion.
  • Strong background in scalable data architectures and modern data warehousing.
  • Excellent stakeholder management across technical and non-technical audiences.

Responsabilités

  • Own post-sales technical engagement for Commercial and Enterprise accounts.
  • Drive adoption and ongoing usage of the platform for sustained success.
  • Lead enablement sessions, workshops, and best-practice guidance.
  • Monitor health signals and intervene to prevent churn or contraction.
  • Identify and execute expansion opportunities across business units and regions.

Connaissances

Post-sales experience
Technical ownership
SQL proficiency
Stakeholder management

Outils

Snowflake
Databricks
BigQuery
Redshift
dbt platform

Description du poste

Fivetran is seeking a Customer Solutions Architect in Denver for a hybrid, full-time role. You will own the technical narrative post-go-live, driving adoption, expansion, and platform value for a portfolio of accounts.

You’ll partner with AEs, SEs, and customers to align technical outcomes with business goals. Responsibilities include leading architecture discussions, delivering workshops, and guiding customers through data pipeline best practices.

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