Front is a customer operations platform that helps B2B companies manage complex customer relationships by keeping teams, tools, and conversations in sync. Recognized as a Great Place to Work by various prestigious lists, Front is backed by Sequoia Capital and Salesforce Ventures, having raised $204M. The company is committed to scaling go-to-market strategies through robust data engineering. Front operates on a hybrid model, requiring in-office collaboration on Tuesdays, Wednesdays, and Thursdays.

About the Position

Introduction

Front is a customer operations platform designed for complex B2B needs, ensuring all teams, tools, and customer conversations are synchronized. Unlike platforms for simple interactions, Front specializes in coordinating and contextualizing intricate B2B customer relationships, trusted by over 9,000 companies including Uber Freight, Navan, and Stripe.

At Front, data is crucial for scaling our go-to-market (GTM) strategy. The Data Engineering team builds and maintains reliable data pipelines, models, and tooling to support reporting and analytics for 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 that drive marketing performance reporting and decision-making, covering areas from campaign attribution and pipeline influence to lead-to-revenue analysis. You will develop production-grade pipelines and safeguards to ensure data integrity and self-service insights.

Responsibilities

Build and maintain end-to-end pipelines that extract and transform data from GTM and Marketing systems (e.g., CRM, marketing automation, web analytics, product usage, billing) into our data warehouse for analytical and operational use.
Design and maintain trusted, well-documented data models for marketing and GTM concepts such as leads, contacts, accounts, opportunities, campaign touchpoints, attribution, pipeline influence, bookings, and churn.
Collaborate with Marketing Ops, Growth Marketing, RevOps, and Analytics teams to define metrics, align definitions, and ensure consistent reporting across various tools and teams.
Enhance data quality and observability through implementation of monitoring, alerting, SLAs, automated tests, and incident response for critical marketing datasets.
Enable self-service analytics by creating curated datasets, a semantic layer, and secure access patterns for confident data exploration by stakeholders.
Optimize performance and cost through tuning warehouse workloads, incremental processing patterns, and storage/compute strategies for large-scale datasets.
Contribute to platform enhancements such as orchestration, CI/CD for data, access controls (RBAC), and PII handling to ensure data security and compliance.

Requirements

7+ years of dedicated data engineering, analytics engineering, or similar experience in building and debugging scalable production data pipelines and data models.
Strong SQL proficiency and experience writing clean, testable code in Python (or a comparable language for data engineering).
Proven experience building data models and pipelines for large datasets (hundreds of TB to petabyte scale), with a focus on performance and cost optimization.
Experience with modern data stacks and warehouses/lakes (e.g., Snowflake, Redshift, Databricks) and orchestration tools (e.g., Airflow), along with modern SQL transformation practices (e.g., dbt).
Ability to navigate ambiguity, translating complex or competing executive goals into clear, actionable, and robust data solutions.
Strong foundational knowledge in data quality, testing, and debugging, including the ability to trace issues across diverse sources, transformations, 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.
Demonstrated mentorship and commitment to engineering excellence: raising technical standards, establishing team guidelines for dbt/SQL quality and CI/CD, and supporting colleagues through code reviews and pairing.
Strong collaboration and empathy skills: able to listen, ask pertinent questions, and develop solutions that balance stakeholder needs with platform integrity.
A champion of data privacy and integrity, always acting in the best interest of data consumers.

Benefits

Competitive salary
Equity (the company is post-Series D and backed by top US VCs)
Private health insurance, with options at no cost to employees
Paid parental leave
Flexible time off policy
Flexibility to work from home on Monday and Friday (unless fully remote role)
Mental health support through Workplace Options
Family planning support through 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)