Staff Data Engineer, Tech Lead

Mudflap; United States; Hybrid; USD 180,000 - 235,000; Full time

Benefits offered by this job

Commuter benefits

Lunches and snacks

Collaborative, high-growth environment

Job summary

Mudflap is rebuilding the data foundation for our rapidly growing fintech platform that powers the trucking industry. We seek a Staff Data Engineer, Tech Lead, to own architecture, governance, and scalability across a cloud data platform.

You'll partner with product, operations, finance, and risk to translate needs into robust data solutions, mentor engineers, and push the adoption of AI tooling while leading end-to-end projects.

Qualifications

• Bachelor's or Master's degree in Computer Science or related field.
• 8+ years of data engineering experience with 3+ years in senior or staff roles.
• Hands-on with AI-assisted and agentic engineering.
• Proven data ingestion frameworks (batch and streaming).
• Strong SQL and Python with cloud data platforms experience.

Responsibilities

• Act as the overall technical leader for data engineering and architecture.
• Lead cross-functional initiatives end to end from scoping to delivery.
• Drive organization-wide technical alignment with staff leadership.
• Own AI and agentic workflows in data engineering.
• Architect and scale ingestion frameworks for diverse data sources.
• Own ML deployment architecture in partnership with data science.
• Define data schemas, governance, and lifecycle management.
• Mentor engineers and promote code quality and design reviews.
• Drive adoption of modern tooling for orchestration and lineage.

Skills

SQL

Python

Leadership

Data modeling

Communication

Education

Bachelor's or Master's in Computer Science or related field

Tools

Snowflake

BigQuery

Databricks

Redshift

Job description

At Mudflap, we're modernizing the $1 trillion trucking industry, the backbone of the U.S. economy. By helping over 100,000 underserved small businesses save over $1.5 billion on fuel, Mudflap has quickly grown into a profitable fintech platform that processes billions in annual payment volume.

We are building the data foundation that powers smarter, faster decisions across the business. As a Staff Data Engineer, Tech Lead , you'll play a mission-critical role in designing, scaling, and governing our modern, cloud-based data platform - and in leading the team and the projects that deliver it. Your expertise in data ingestion, data architecture, schema design, and governance will help shape how data flows across our systems and is leveraged by teams company-wide.

This is a unique opportunity to set standards, drive technical direction, and ensure the quality, consistency, and scalability of the data platform that hundreds of thousands of SMB trucking companies rely on - and love - to run their businesses every day. You'll lead projects end to end, working directly with product leaders and business leaders across Operations, Finance, and Risk, etc. to turn their needs into clear technical requirements. You'll partner with engineering leadership on how the team plans and operates, work alongside staff engineering leadership to drive alignment across the organization, and set our direction on how AI and agentic workflows reshape data engineering. It's an ideal role for someone looking to grow their leadership scope while staying deeply technical.

Work Location:

This role is based in Palo Alto and involves a hybrid work approach, balancing in-office collaboration with the ability to work remotely.

To support our team, we offer:

• Commuter benefits to ease your travel
• Lunches and snacks to keep you fueled
• A collaborative, high-growth environment where you'll work closely with talented teammates across the company

Expectations (In this role, you will):

Act as the overall technical leader for the data engineering, owning architecture direction, setting standards, and making the design decisions that unblock the team.

Lead high-impact, cross-functional initiatives end to end - scoping the problem, sequencing the work, and driving it to delivery. Partner with manager on team and project management processes, from sprint planning to team processes.

Drive organization-wide technical alignment, representing data in staff engineering leadership forums and partnering with engineering leaders across the company on shared initiatives.

Serve as our thought leader on AI and agentic workflows in data engineering, and deepen the team's AI transformation.

Architect and scale ingestion frameworks to bring diverse data sources (internal systems, third-party APIs, streaming events) into our platform in a reliable, high-throughput way.

Own and evolve the architecture for machine learning deployment in partnership with our data science teams, building the infrastructure and patterns that move models from prototypes to reliable, monitored production serving.

Define and enforce data schemas, and establish best practices for data modeling, schema evolution, and data lifecycle management, to ensure data integrity, discoverability, and interoperability across teams.

Own data governance strategy, including data quality, metadata management, lineage, and compliance with security and privacy requirements.

Design and optimize system-wide data architecture to support analytics, agentic, and operational use cases at scale.

Partner with product, engineering, analytics, compliance, and business functions (Operations, Finance, Risk, etc.) to align data architecture with business and regulatory needs.

Mentor other engineers, leading by example in code quality, architecture reviews, technical design discussions, and effective use of AI tooling.

Drive adoption of modern tooling for orchestration, lineage tracking, and cataloging to improve platform robustness and usability.

Experience (What we look for):

• Bachelor's or Master's degree in Computer Science (or related technical field)
• 8+ years of data engineering experience, with at least 3+ years in senior or staff-level roles.
• Track record as a tech lead, owning technical direction for a team or major platform area and delivering multi-quarter initiatives with several engineers and cross-functional partners.
• Hands-on experience with AI-assisted and agentic engineering, with a clear point of view on where agents create leverage in data work and where they don't.
• Proven expertise in data ingestion frameworks (batch and streaming)
• Deep experience with cloud-native data platforms (Snowflake, BigQuery, Databricks, or Redshift).
• Experience building or owning machine learning deployment infrastructure - feature pipelines, model serving, and production monitoring - in partnership with data science teams.
• Strong command of SQL and Python, with ability to design performant, reusable compo