Lead Software Engineer — Analytics & Reporting
Confirmed on the employer's own hiring board on Sep 27, 2026. First seen by Alion on Sep 10, 2026.
About the role
We’re splitting our Analytics & Reporting group into two focused teams: a Data Engineering team that owns data acquisition and the company-wide data platform, and an App team that owns everything our customers touch, including the data-serving APIs, the Analytics section of the Uberall platform, and the reports that turn location data into decisions.
We’re hiring the lead for the App team. You’ll be a player-coach: you’ll manage and grow a team of six engineers (3 backend, 2 frontend, 1 QA), partner daily with two Product Managers, a Designer, and a UX Researcher, and stay meaningfully hands-on. Roughly 20-30% of your time is spent on architecture, code reviews, and code. You’ll work as a peer to the Lead Data Engineer, with a close, explicit contract between your team’s customer-facing products and the data platform that feeds them.
This is a role for someone who has led before and still loves to build.
What you’ll own
Customer-facing data APIs: the public APIs enterprise customers use to access their data, and the microservices behind them.
The Analytics product: the Analytics section of the Uberall platform, including its frontend, filtering experience, and the APIs powering custom reports.
Customer reporting: the QuickSight reports our customers rely on, and the gold-layer datasets that feed them. Your team owns its data models end-to-end, built on the self-serve platform the Data Engineering team provides.
Production: you build it, you run it. Your team deploys and operates its own services (Kubernetes on AWS), owns its observability and SLOs, and runs its own on-call rotation. You lead the incident process.
The team: hiring, growth, 1:1s, delivery, and the engineering culture of a newly formed team. With one QA engineer among six, you’ll make quality everyone’s job.
What you’ll do in your first year
Stand up the App team as a healthy, autonomous unit: clear ownership, sustainable delivery cadence, and strong working relationships with Product, Design, and the Data Engineering team.
Co-own, from the application side, the in-flight migration of data pipelines out of our monolith and into the Data Engineering stack. Your backend engineers keep those pipelines running and hand them over cleanly, so your team can focus fully on APIs and product.
Drive the modernization of our backend estate: the stack is consolidating on Kotlin, and legacy Groovy and Scala services need a pragmatic path forward.
Shape the next chapter of Analytics: AI-assisted insights in the product, and exposing your team’s data and APIs for consumption by AI agents through the company’s data gateways.
What we’re looking for
7+ years of software engineering experience, with deep backend expertise on the JVM (Kotlin strongly preferred; our stack: Kotlin, Spring Boot, Postgres).
2+ years leading an engineering team with genuine people responsibility (hiring, 1:1s, performance, and delivery) while staying hands-on.
Experience designing, operating, and evolving microservices in production: Kubernetes, AWS, observability, SLOs, and incident management. You’ve run an on-call rotation, not just been on one.
Strong SQL and analytical data modeling skills. Your team owns the datasets behind customer reports, and you can reason about them credibly.
Experience with RESTful, service-oriented architectures serving external customers at scale.
Enough frontend fluency (React, TypeScript) to lead frontend engineers well. You review their thinking, not their semicolons.
Experience navigating legacy systems and migrations. You can modernize without stopping the world.
Professional working English (read, write, speak) and comfort collaborating across a remote, distributed organization.
Nice to have
Experience with Groovy, Scala, or Python - the languages of our existing services.
Experience with BI tooling (Amazon QuickSight or similar) and data warehouse platforms (Databricks, Snowflake, Redshift).
Experience building LLM-powered product features or exposing data/APIs to AI agents.