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 components. - Hands-on knowledge of data governance frameworks (quality checks, lineage, cataloging, security, compliance). - Strong understanding of data warehouse methodologies, dimensional and normalized modeling, and distributed system design. - Experience with orchestration tools (Airflow, Dagster, Prefect) and metadata/catalog platforms (dbt, DataHub, Amundsen, or similar). - Ability to set long-term technical vision while driving execution on near-term deliverables. - Excellent communication and leadership skills, with a track record of mentoring engineers and influencing cross-functional stakeholders and senior leaders. Perks and Benefits (What we offer): - Competitive salary and equity in a high-growth startup - Multiple health benefit options - Responsible Time Off - 401(k) matching - Opportunities and support for major career growth - Annual Company offsite event (Mudfest!) The base salary range for this role is $243,000 - $285,000. This information reflects a base salary range for this position based on current market data, which may be subject to change as new market data becomes available. The candidate’s skills, experience, and other relevant factors will determine the exact compensation. Company Overview (Who we are): Mudflap is on a mission to transform the trucking and logistics industry by leveling the playing field for owner operators and small fleets. Backed by top-tier venture investors, including QED, Matrix Partners, Commerce Ventures, NFX, and 500 Startups and included in the Forbes Fintech 50 list, Mudflap offers fleet fuel management solutions. Our core team hails from Disney, Uber, Procore, DoorDash, Google, Meta, Capital One, Affirm and Brex. Here are the core values that we believe in and look for in new teammates: - Be Customer Obsessed: We deeply understand customer needs and put our customers at the center of everything we do - Make it Count: Act like an owner by focusing on the impact of your work - Find a Way: Be a creative problem solver who pushes past roadblocks to win for our customers and our teammates - Sweat the Details: We keep our standards high and achieve them by paying attention to every detail - Be Curious: Use a growth mindset to question assumptions, take calculated risks and stretch the boundaries of what’s possible

Requirements

Must-haves

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.
Lunches and snacks to keep you fueled
A collaborative, high-growth environment where you’ll work closely with talented teammates across the company
Act as the overall technical leader for the data engineering, owning architecture direction, setting standards, and making the design decisions that unblock the team.
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.
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.
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 components.
Hands-on knowledge of data governance frameworks (quality checks, lineage, cataloging, security, compliance).
Strong understanding of data warehouse methodologies, dimensional and normalized modeling, and distributed system design.
Experience with orchestration tools (Airflow, Dagster, Prefect) and metadata/catalog platforms (dbt, DataHub, Amundsen, or similar).

Nice-to-haves

None flagged.

Application prep

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aimachine learningsqlpython

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Benefits

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Vision

401(K)