Staff Data Engineer

Mudflap•Palo Alto, CA

5d•$243,000 - $285,000•Hybrid

About The Position

Mudflap is hiring a Staff Data Engineer and Tech Lead to design, scale, and govern its cloud-based data platform. The role sets technical standards and direction for data engineering and leads high-impact projects from scoping through delivery. It works closely with product and business leaders across Operations, Finance, Risk, and other functions, as well as engineering leadership and data science teams. The position also guides the team’s adoption of AI and agentic workflows in data engineering. The role owns data ingestion and platform architecture, machine learning deployment infrastructure, data schemas and modeling practices, and data governance. It supports analytics, agentic, and operational use cases while ensuring data quality, discoverability, interoperability, security, privacy, and compliance. The position is based in Palo Alto with a hybrid work approach that combines in-office collaboration and remote work.

Requirements

Bachelor’s or Master’s degree in Computer Science or a related technical field.
At least 8 years of data engineering experience, including 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 do not.
Proven expertise in batch and streaming data ingestion frameworks.
Deep experience with cloud-native data platforms such as Snowflake, BigQuery, Databricks, or Redshift.
Experience building or owning machine learning deployment infrastructure, including feature pipelines, model serving, and production monitoring, in partnership with data science teams.
Strong command of SQL and Python, including the ability to design performant, reusable components.
Hands-on knowledge of data governance frameworks, including quality checks, lineage, cataloging, security, and compliance.
Strong understanding of data warehouse methodologies, dimensional and normalized modeling, and distributed system design.
Experience with orchestration tools such as Airflow, Dagster, or Prefect, and metadata or catalog platforms such as dbt, DataHub, Amundsen, or similar.
Ability to set a long-term technical vision while driving near-term deliverables.
Excellent communication and leadership skills, with a track record of mentoring engineers and influencing cross-functional stakeholders and senior leaders.

Responsibilities

Act as the overall technical leader for data engineering, owning architecture direction, setting standards, and making design decisions that unblock the team.
Lead high-impact cross-functional initiatives end to end, including scoping problems, sequencing work, and driving delivery; partner with the manager on sprint planning and team processes.
Drive organization-wide technical alignment, represent data in staff engineering leadership forums, and partner with engineering leaders on shared initiatives.
Guide the use of AI and agentic workflows in data engineering and deepen the team’s AI transformation.
Architect and scale reliable, high-throughput ingestion frameworks for internal systems, third-party APIs, and streaming events.
Own and evolve machine learning deployment architecture with data science teams, including infrastructure and patterns for reliable, monitored production serving.
Define and enforce data schemas and best practices for data modeling, schema evolution, and data lifecycle management.
Own data governance strategy, including data quality, metadata management, lineage, and security and privacy compliance.
Design and optimize system-wide data architecture for analytics, agentic, and operational use cases at scale.
Partner with product, engineering, analytics, compliance, and business functions to align data architecture with business and regulatory needs.
Mentor engineers through code quality, architecture reviews, technical design discussions, and effective use of AI tooling.
Drive adoption of orchestration, lineage-tracking, and cataloging tools to improve platform robustness and usability.