Senior Data Engineer

Front is the customer operations platform built for B2B complexity, keeping every team, tool, and customer conversation in sync so companies can scale without losing connection. Others handle simple interactions. Front handles the coordination and context behind complex B2B customer relationships. Over 9,000 companies, including Uber Freight, Navan, and Stripe, rely on Front because it's the only one that can run the operational layer that makes customer-facing work actually succeed.

Backed by Sequoia Capital and Salesforce Ventures, Front has raised $204M from leading venture capital firms and independent investors including top executives at Atlassian, Okta, Qualtrics, Zoom, and PagerDuty. Front has received numerous Great Place to Work accolades, including 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 at the center of how we scale go-to-market (GTM). Our Data Engineering team builds and operates the pipelines, models, and tooling that power reliable reporting and analytics for Sales, Marketing, Partnerships, and RevOps.

This Senior Data Engineer role is based in San Francisco (hybrid) and will be a key partner to the Marketing org. You’ll own the datasets and semantic layer that power marketing performance reporting and decision-making — from campaign attribution and pipeline influence to lead-to-revenue analysis — and you’ll build the production-grade pipelines and guardrails that make those insights trustworthy and self-serve.

• 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.

• 7+ years of dedicated data engineering, analytics engineering, or related experience building and debugging production data pipelines and data models at scale.

• Strong SQL plus experience writing clean, testable code in Python (or a similar language used for data engineering).

• Experience building data models and pipelines on top of 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.