Data Engineer

Higgsfield AI

. Design, develop, and maintain scalable data pipelines integrating CRM, billing, product analytics, and other business-critical systems .

Posted 11/7/2026full-timeSan Francisco • California • United StatesMid-LevelSenior💰 $200,000 - $260,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining scalable data pipelines, integrating various business-critical systems, and ensuring data accuracy and consistency. Proficient in SQL and Python, with hands-on experience in ETL/ELT processes and modern data warehousing solutions.

Highest-signal resume keywords

Data EngineeringSQL ProficiencyPython ProficiencyETL/ELT Pipeline DevelopmentData Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringSQLPythonETLELTData ModelingData ArchitectureData QualityData TransformationData Orchestration

Soft Skills

Strong Communication SkillsCollaboration with Nontechnical Stakeholders

Tools & Technologies

SnowflakeBigQueryRedshiftDbtAirflowDagsterSalesforceHubSpotStripe

Industry Keywords

B2B SaaS MetricsARRPipelineConversionRetentionChurnExpansion

Tech Stack

Tools & technologies

AirflowAmazon RedshiftBigQueryETLPythonSnowflakeSQL

About the role

Key responsibilities & impact

Design, develop, and maintain scalable data pipelines integrating CRM, billing, product analytics, and other business-critical systems
Connect product usage, account activity, sales pipeline, contracts, and revenue data to create a consistent view of B2B customers
Build reliable, well-documented data models supporting pipeline analytics, revenue reporting, customer segmentation, retention, and expansion
Partner with Sales, RevOps, and Finance to support forecasting, territory planning, account prioritization, sales performance, and revenue attribution
Establish visibility into enterprise customer adoption and product usage to enable insights into conversion, engagement, and expansion opportunities
Implement automated validation, monitoring, documentation, and governance to ensure data accuracy and consistency across systems
Identify opportunities to improve pipeline performance, automation, and infrastructure as the business grows
Collaborate with Product and other cross-functional teams to establish a trusted foundation for reporting, forecasting, customer intelligence, and strategic decision-making

Requirements

What you’ll need

6-8+ years of experience in Data Engineering, Analytics Engineering, or a closely related technical role
Strong proficiency in SQL and Python
Experience building production-grade ETL/ELT pipelines
Experience with modern data warehouses such as Snowflake, BigQuery, or Redshift
Hands-on experience with data transformation and orchestration tools such as dbt, Airflow, Dagster, or similar technologies
Experience integrating data from Salesforce, HubSpot, Stripe, or other CRM and billing platforms
Strong understanding of data modeling, data architecture, and data quality best practices
Familiarity with B2B SaaS metrics, including ARR, pipeline, conversion, retention, churn, and expansion
Ability to translate ambiguous business requirements into scalable technical solutions
Strong communication skills and experience partnering directly with nontechnical stakeholders
Must be based in the San Francisco Bay Area or willing to relocate
Able to work in the San Francisco office at least 3 days per week
Expected to maintain sufficient overlap with the relevant team’s time zone

Benefits

Comp & perks

Bonus eligible and may participate in the Company's discretionary annual bonus program
Eligible to participate in the company’s stock option program
Medical, dental, and vision insurance
Competitive 401(k) retirement plan with company matching
Additional benefits and perks
Opportunity to work on ambitious AI products alongside a highly experienced, fast-moving, and international team
Significant ownership, direct impact, and opportunities for professional growth as the company scales
Hybrid work arrangement with remaining days worked remotely