Senior Support Engineer

Afresh Technologies; United States; Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Health plans

Parental leave

Equity packages

401k matching

Flexible vacation policy

Professional development program

Work from home stipend

Job summary

Afresh Technologies, a leader in fresh grocery technology, seeks a Principal Support Engineer to own the most complex escalations and drive end-to-end investigations across all Afresh products. You will build agentic AI workflows and tooling, ship Claude skills for Tier 1 resolution, and coordinate data-issue investigations across 200 products.

You will mentor engineers, define success metrics, and present findings to Product and Data Engineering leadership, contributing to strategic OKRs and

Qualifications

• 5+ years in technical support engineering, solutions engineering, data engineering, or equivalent role with root-cause ownership.
• Ability to own complex cross-functional initiatives end-to-end and secure commitments.

Responsibilities

• Own the most complex escalations end-to-end across Afresh products.
• Build tooling and AI workflows to deflect future volume.
• Lead cross-functional investigations with Solutions Engineering, Data Engineering, Product Engineering, and Operations.
• Mentor Tier 1/2 engineers and contribute to playbooks and docs.
• Present root-cause analyses and measured impact to leadership.

Skills

SQL

Python

Databricks

Looker

AI tools (Claude, Stylo)

Root-cause analysis

Tools

Zendesk

Stylo

Job description

• As a Principal Support Engineer at Afresh, you are the person who understands how our platform actually behaves for our grocery partners
• You have deep technical support, solutions, or data engineering experience, you communicate with equal clarity to a store director and a staff engineer, and you are excited to reduce fresh food waste in the grocery industry
• Support Engineering is the complex technical investigation arm of our Support organization — the last stop for internal escalations to resolve customer-facing problems across all Afresh products
• You will own the complex escalations that others cannot resolve and then work upstream to eliminate or minimize future recurrence; quantifying impact, building tooling and agentic AI workflows to deflect future volume, and then proactively driving the cross-functional efforts to eliminate the root cause
• This is a senior individual contributor role, and the scope of this role is deliberately broad
• You will be handed goals rather than tasks and you are expected to quickly and autonomously develop deep expertise across our products and the complex data relationships behind them
• You will own and lead initiatives from end to end, building influence and alignment with other teams to achieve your goals
• A typical week might involve building an agentic AI support workflow in Stylo, shipping a Claude skill that lets Tier 1 Support resolve issues with a new product feature, coordinating a team-wide investigation of data issues across 200 products, and then presenting the root cause analysis and measured impact of that investigation to Product and Data Engineering leadership
• Own our most complex escalations end to end — data mapping defects, recommendation and forecast anomalies, and integration or data-feed failures
• Identify and lead large initiatives that deliver critical impact against department-level OKRs, such as raising our ticket deflection rate, reducing average handle time, or reducing escalations to cross-functional teams. You will work autonomously — scoping the work yourself, defining what success looks like, and communicating progress and blockers without being asked
• Define and report the numbers that prove impact: baseline before you build, quantify after, and publish on a regular cadence in channels where the appropriate teams and leaders can see it
• Build the tooling that removes the work — SQL and Python, Databricks and Looker dashboards and notebooks, Stylo workflows, Claude and Cowork skills, and lightweight automations that take manual effort off Tier 1 Support and Operations
• Drive cross-functional change with teams that don’t report to you, partnering with Solutions Engineering, Data Engineering, Product Engineering, and Operations to fix the systems generating ticket volume — securing shared ownership, holding regular communication cadence, and raising flags visibly when progress stalls
• Multiply the team rather than absorb its hardest work: mentor Tier 1 and Tier 2 engineers, lead training, and write the playbooks and source-of-truth documentation that let others resolve what only you can resolve today
• Be visible outside Support — present in Tech Demos, post data-driven updates in company-wide channels, join account reviews, and represent Support in cross-functional forums
• Master partner-specific behavior across our retailers and their banners, and serve as the escalation point when a judgment call needs to reach Product, Engineering, or the customer directly

Benefits

• Comprehensive health plans
• Generous parental leave
• Equity packages
• 401k matching
• Flexible vacation policy
• Professional development program
• Work from home stipend

Advanced SQL. You reason comfortably about joins across large, imperfect datasets, and you validate findings against the authoritative source rather than the most convenient table5+ years in technical support engineering, solutions engineering, data engineering, or a comparable role where you personally owned root-cause analysis on production dataThe following represent attributes our ideal candidate possesses. Self-direction. You source your own answers, set your own milestones, and do not need oversight to make progressProficiency building with AI tools such as Claude or other agentic platforms, paired with sound judgment about where automation can be trusted and where it cannotClear, calm written communication, and the instinct to teach rather than correct when helping less experienced teammatesDemonstrated ability to own complex cross-functional initiatives end to end and land them — including the uncomfortable parts: securing commitments from other teams, absorbing friction, and driving to a decisionScripting ability in Python or similar to automate investigation and data workA measurement instinct. You define success numerically before you start, and you speak to results rather than activityComfort with ambiguity and a bias toward shipping an imperfect, iterative solution over waiting for a complete oneA track record of mentoring engineers and raising the technical bar of a teamExperience supporting ML-driven products, and the ability to distinguish whether an anomaly originates in data inputs, model behavior, or application logicExperience with Databricks, Zendesk, Stylo, or workflow automation platformsGrocery, retail, or supply chain domain knowledge — hands-on familiarity with ordering, replenishment, assortment, or fresh department operations is a significant advantage