Front is the customer operations platform built for B2B complexity. It synchronizes teams, tools, and customer conversations to enable companies to scale without losing personal connection. Front focuses on the coordination and context required for complex B2B customer relationships, serving over 9,000 companies globally. They are backed by Sequoia Capital and Salesforce Ventures, having raised $204M. Front is recognized as a Great Place to Work, appearing on Built In's, USA Today's, Y Combinator's, Fortune's, Inc. Magazine's, and Forbes' best workplaces lists.

About the Position

Introduction

Front is a customer operations platform designed for B2B complexity, ensuring teams, tools, and customer conversations are synchronized for scalable growth. Unlike simpler interaction handlers, Front excels in coordinating and contextualizing intricate B2B customer relationships. Trusted by over 9,000 companies including Uber Freight, Navan, and Stripe, Front powers the operational layer crucial for successful customer-facing work.

Backed by Sequoia Capital and Salesforce Ventures, Front has secured $204M from prominent VC firms and independent investors. The company has received numerous Great Place to Work accolades, such as Built In's 100 Best Midsize Places to Work in SF 2025, Top Places to Work by USA Today 2025, Y Combinator's list of Top Companies in 2023, #4 on Fortune’s Best Workplaces in the Bay Area™, Inc. Magazine's 2022 Best Workplaces list, and Forbes Best Startup Employers 2022 List.

At Front, data is central to scaling our go-to-market (GTM) efforts. The Data Engineering team builds and maintains pipelines, models, and tools for reliable reporting and analytics across Sales, Marketing, Partnerships, and RevOps.

This Senior Data Engineer role, based in San Francisco (hybrid), will be a key partner to the Marketing organization. You will be responsible for the datasets and semantic layer supporting marketing performance reporting and decision-making, including campaign attribution, pipeline influence, and lead-to-revenue analysis. You will build production-grade pipelines and safeguards to ensure trustworthy and self-serve insights.

Responsibilities

Build and maintain end-to-end pipelines that move and transform data from GTM + Marketing systems (e.g., CRM, marketing automation, web analytics, product usage, billing) into our warehouse for analytics and operational use.
Design and own trusted, well-documented data models for marketing + GTM concepts such as leads, contacts, accounts, opportunities, campaign touchpoints, attribution, pipeline influence, bookings, and churn.
Partner with Marketing Ops, Growth Marketing, RevOps, and Analytics to define metrics, align definitions, and ensure consistent reporting across teams and tools.
Improve data quality and observability by implementing monitoring, alerting, SLAs, automated tests, and incident response practices for critical marketing datasets.
Enable self-serve analytics by building curated datasets, a semantic layer, and safe access patterns that let stakeholders explore data with confidence.
Optimize performance and cost by tuning warehouse workloads, incremental processing patterns, and storage/compute strategies for large-scale datasets.
Contribute to platform improvements such as orchestration, CI/CD for data, access controls (RBAC), and PII handling to keep our data secure and compliant.

Requirements

7+ years of dedicated data engineering, analytics engineering, or related experience building and debugging production data pipelines and data models at scale.
Strong SQL skills and experience writing clean, testable code in Python (or a similar language used for data engineering).
Experience building data models and pipelines on large datasets (hundreds of TB through petabyte scale), with attention to performance and cost.
Experience with modern data stacks and warehouses/lakes (e.g., Snowflake, Redshift, Databricks) and orchestration tools (e.g., Airflow) as well as modern SQL transformation practices (e.g., dbt).
Ability to navigate ambiguity: translating vague, complex, or competing goals from executive stakeholders into clear, actionable, and robust data solutions.
Strong fundamentals in data quality, testing, and debugging, including the ability to trace issues across sources, transforms, and downstream dashboards.
Experience designing and implementing access control patterns at scale (RBAC, masking policies, row access policies, role hierarchies) in Snowflake or similar platforms.
Mentorship & engineering excellence: raising the technical bar, establishing team standards for dbt/SQL quality and CI/CD, and supporting others through reviews and pairing.
Strong collaboration and empathy: you listen, ask the right questions, and build solutions that balance stakeholder needs with platform integrity.
You champion data privacy and integrity, and act in the best interest of data consumers.

Benefits

Competitive salary
Equity (post-series D, backed by top US VCs)
Private health insurance, including plan options at no cost to employees
Paid parental leave
Flexible time off policy
Flexibility to work from home Monday and Friday (unless fully remote role)
Mental health support with Workplace Options
Family planning support with Maven
$100 per month Lifestyle Stipend for fitness, health, wellness, and other activities
Wellness Days - additional day off in months without holidays
Winter Break - offices closed from Christmas to New Year's Day