Foodsmart - Staff Data Engineer
Requirements
• 10+ years of professional experience in data engineering, with a demonstrated track record of driving large-scale, org-wide data platform initiatives. • Proven experience functioning at a staff or principal level—influencing architecture decisions beyond a single team or project. • Deep expertise in modern cloud data warehouses (Snowflake, Redshift, or BigQuery) and distributed data processing frameworks. • Advanced SQL skills and strong experience with relational, columnar, and NoSQL database systems. • Expert-level proficiency in data pipeline and workflow orchestration tools (e.g., Apache Airflow, Dagster) and experience designing for operational maturity (observability, alerting, recovery). • Strong programming background in Python, Scala, or Java for data processing, automation, and platform tooling. • Experience in backend development with proficiency in TypeScript, Java, Python, Node.js, and SQL-based RDBMS such as • Deep experience designing data models and schemas for both operational and analytical use cases, including dimensional modeling and data mesh or data lakehouse patterns. • Familiarity with healthcare data standards (FHIR, HL7) or other regulated data domains strongly preferred. • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field; Master's degree preferred. • $180,000 - $215,000 a year • Role: Staff Data Engineer • Base Salary Range: $180,000/yr to $215,000/yr + bonus + benefits • Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries at our headquarters in San Francisco, California. Individual pay is determined by work location, job-related skills, experience, and relevant education or training.
Responsibilities
• Define and own the strategic architecture of Foodsmart's data platform, setting the technical direction for scalable, reliable, and governed data infrastructure. • Lead the design and implementation of next-generation data pipelines, data models, and platform capabilities that support advanced analytics and machine learning at scale. • Establish and champion company-wide standards for data quality, governance, security, and compliance—including automated frameworks for validation, enrichment, and lineage tracking. • Act as a cross-functional technical leader, partnering with engineering, product, data science, and business stakeholders to align data platform capabilities with organizational goals. • Identify systemic bottlenecks and drive platform-wide improvements in performance, reliability, and developer productivity. • Mentor and level up senior and junior engineers alike; influence hiring decisions, technical roadmaps, and data platform strategy at the organizational level. • Serve as an internal authority on data engineering best practices, contributing to broader engineering culture through documentation, design reviews, and knowledge sharing. • A technical visionary who can balance long-term platform thinking with pragmatic, high-impact delivery. • Deeply quality-oriented, with an organizational sense of ownership over data reliability, observability, and accessibility. • A trusted partner to senior leadership, able to translate complex technical tradeoffs into clear business implications. • Experienced navigating regulated environments with a rigorous approach to secure and compliant data handling. • A force multiplier—you make the engineers around you significantly more effective.
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