Staff Data Engineer

About the role

Front is seeking a Staff Data Engineer to lead the development of our analytics infrastructure. You will design scalable data models and pipelines while fostering a self-service environment for stakeholders across the organization. In this capacity, you will own the end-to-end architecture of critical data products, ensuring that data flows reliably and efficiently from source systems to consumption. You will partner closely with cross-functional teams to translate ambiguous business needs into robust technical solutions that drive actionable insight. The role requires a balance of deep technical execution and strategic thinking to solve complex data challenges at scale. You will champion data quality and governance, establishing standards that enable the business to trust the analytics they rely on. This position is an opportunity to shape the data culture of a growing company and define best practices for engineering and analytics. You will mentor other engineers and analysts by example, promoting clean code, rigorous testing, and thoughtful system design.

Key facts

What you'll do

• Architect end-to-end data pipelines that prioritize performance and reliability while meeting evolving business demands.
• Build automated platforms to enable self-service data consumption for internal users, reducing dependency on specialized teams.
• Maintain data quality standards and ensure all team service level agreements are met through monitoring and proactive issue resolution.
• Develop data schemas and optimize query performance for large, complex datasets to support fast and accurate analysis.
• Collaborate with data scientists, analysts, and business stakeholders to drive data best practices and align on definitions.
• Manage data security and availability across multiple regions and data centers, ensuring compliance and risk mitigation.
• Establish monitoring and logging frameworks to ensure system traceability and rapid diagnosis of issues in production.
• Evaluate and manage data vendors and technical tooling to select solutions that maximize value and integration capabilities.
• Apply AI tools to improve development speed, code quality, and automation workflows, leveraging intelligent tooling where appropriate.
• Design and implement data models that support both operational reporting and long-term analytical initiatives.
• Partner with product teams to embed analytics directly into product workflows, enabling data-driven decision-making.
• Optimize infrastructure costs while maintaining high standards for performance, scalability, and maintainability.
• Lead code reviews and technical documentation to ensure knowledge sharing and consistency across the data team.
• Participate in on-call rotations to support production systems and respond to data incidents as they arise.

Requirements

• BS/BA in Computer Science, Mathematics, or a related technical field, or equivalent experience.
• Minimum 5 years of professional experience in data engineering or deep ETL development.
• Proficiency in managing Snowflake data warehouses, including scaling, security, and optimization.
• Strong programming skills with a focus on modular and maintainable code that can be easily tested and extended.
• Advanced expertise in Python and SQL for handling large datasets and complex transformation logic.
• Experience with at least one big data technology such as Spark, Flink, Presto, EMR, HDFS, or Redshift.
• Practical knowledge of workflow management tools, specifically Airflow, for orchestrating complex pipelines.
• Ability to manage multiple projects simultaneously while maintaining a service-oriented mindset and clear prioritization.
• Understanding of data modeling techniques, including dimensional modeling and data vault approaches where applicable.
• Familiarity with version control practices and infrastructure as code principles for data pipelines.
• Strong attention to detail and the ability to troubleshoot issues in distributed data systems.
• Willingness to collaborate closely with engineering, product, and analytics teams in a fast-paced environment.

Skills & tools

• Snowflake
• Python
• SQL
• Airflow
• Big data technologies (Spark, Flink, Presto, EMR, HDFS, or Redshift)

Nice to have

• Experience with cloud platforms and infrastructure provisioning relevant to data workloads.
• Knowledge of data observability tools and practices for monitoring pipeline health.
• Exposure to containerization and orchestration tools that support data engineering workflows.
• Understanding of data governance, privacy, and regulatory compliance considerations.
• Experience with business intelligence tools and dashboarding practices.

Practical notes

• Front follows a hybrid work model with in-office attendance required Tuesday through Thursday.
• Benefits include private health insurance, paid parental leave, flexible time off, and mental health support via Workplace Options.
• Additional perks include family planning through Maven, a $100 monthly lifestyle stipend, designated wellness days, and a company-wide winter break.

About the company

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.