DIRECTOR, DATA ENGINEERING & ANALYTICS

Second Harvest of Silicon Valley•San Jose, CA

1d•$162,954 - $191,710•Hybrid

About The Position

Second Harvest of Silicon Valley is building a small enterprise data function as its operations, programs, and systems become more connected. The Director will take work already underway and develop it into a durable internal capability. This builder-leader role combines setting direction, decision rights, delivery discipline, and team standards with hands-on review and problem-solving across architecture, data models, SQL, Python, quality controls, and production issues. The role is responsible for creating a trusted source-to-decision system spanning food flow, transportation, programs, partners, finance, fundraising, and workforce data, while keeping the central team appropriately small. The Director leads an engineering-led team and enables a federation of departments to participate in enterprise data analytics. The position works across executive priorities, technical reviews, warehouse and program workflows, and stakeholder delivery commitments. The position is based at Second Harvest of Silicon Valley’s Curtner Warehouse in San Jose and is hybrid. The employee’s regular remote work site must be within driving distance of a Second Harvest location and within California. Starting in March 2027, onsite work at the new central hub in San Jose is required every Tuesday and Thursday. The role is full-time, exempt, 40 hours per week, with some evening and weekend work as necessary. Finalists may be asked to complete a 90-minute case study or online assessment.

Requirements

Eight or more years building and operating data platforms, analytics engineering, or enterprise reporting capabilities.
At least three years of people leadership and management experience.
Demonstrated experience leading a durable cross-functional data program.
Technical foundation sufficient to review and challenge Python, SQL, dimensional models, pipeline design, orchestration, semantic models, quality controls, and production support practices.
Evidence of building a data capability in a non-technology organization where systems, workflows, and ownership were imperfect.
Experience translating operational or program goals into a small portfolio of governed data products with explicit measures of adoption and business value.
Strong judgment about where standardization is essential and where local flexibility is appropriate.
Ability to earn trust with executives, frontline operators, analysts, engineers, IT partners, and vendors without obscuring tradeoffs or overpromising.
Commitment to Second Harvest’s mission and respect for the people represented in the data.

Nice To Haves

Experience with Azure, Microsoft Fabric, Power BI, and modern lakehouse or warehouse environments.
Experience with ERP, CRM, workforce, transportation, logistics, warehouse, grants, or human-services data.
Experience in food distribution, supply chain, healthcare delivery, government, or a mission-driven organization with complex field operations.
A bachelor’s degree or equivalent practical experience; demonstrated capability is valued over a specific academic pedigree.

Responsibilities

Build and lead a small, engineering-led enterprise data and analytics team, establishing clear roles, standards, coaching, and accountability.
Own the enterprise data product portfolio and backlog, generally limiting active material builds to no more than three and making tradeoffs visible to leadership.
Set architecture and engineering standards for ingestion, transformation, dimensional modeling, semantic models, testing, documentation, lineage, releases, and support.
Review material data models, Python, SQL, business rules, Power BI semantic models, and control designs; troubleshoot when accuracy or delivery is at risk.
Establish metric certification and reconciliation practices so reported numbers can be traced to their definitions, sources, transformation logic, owners, and known limitations.
Create the data operating model, including intake, prioritization, business ownership, acceptance, release, issue routing, change control, and lifecycle management.
Partner with IT on platform administration, security, identity, deployment, monitoring, and reliability while owning data product outcomes without duplicating IT infrastructure responsibilities.
Partner with business owners on priorities, definitions, validation, and adoption, keeping metric meaning and operational action with the business while the data team owns engineering rigor.
Manage external data partners through bounded scopes, acceptance criteria, documentation standards, and transfer requirements, reducing avoidable dependency over time.
Build trust through visible delivery, plain-language communication, and disciplined follow-through.