Senior Analytics Engineer

Solv.

About The Position

You’ll be the senior technical owner of Solv’s analytics stack — from raw data through trusted models to the dashboards and tools that internal teams and customers rely on daily. You report to the VP of Data & Analytics on a small, high-leverage team where you set the standards and own the outcomes. Your first priority is making sure the data pipeline is fast, accurate, and stays that way. Our dbt models power everything downstream — internal reporting, partner dashboards, and a growing set of AI-driven analytics tools. When modeling choices create inefficiency, everything slows down. You’ll own the architecture decisions that keep the pipeline fast, accurate, and sustainable as data volume grows. Beyond the foundation, you’ll shape how Solv delivers analytics to customers. Today that’s embedded Looker dashboards. Tomorrow it’s something more ambitious — we’re building AI-powered analytics tools internally and want someone who will help us scale that experience and eventually bring it to customers. You’ll work directly with Solv’s most senior leaders from day one to shape what AI-powered analytics looks like across the company and for our customers.

Requirements

Have 5+ years owning an analytics stack end-to-end — from raw data to trusted models to the tools non-technical teams depend on. You’ve managed business-critical pipelines and seen firsthand how modeling decisions compound downstream.
Have strong data modeling judgment. You’ve made hard architecture calls on real datasets — when to denormalize, when to split, how to balance query performance against maintainability — and can defend those choices.
Are genuinely curious about the business. You don’t wait for requirements; you learn how we go to market, what metrics matter, and why — then build the data infrastructure that drives action.
Communicate clearly across audiences. You can explain a data tradeoff to an executive and a schema decision to an engineer the same afternoon.
Enjoy working on a small, cross-functional team where your work directly shapes how the company and its customers make decisions.
Strong SQL and data transformation framework experience (e.g. dbt).
Experience building and maintaining dashboards and semantic modeling layers in BI platforms (e.g. Looker/LookML).
Experience writing and optimizing models with cloud data warehouses (e.g. Redshift, Snowflake).
Familiarity with ETL pipeline tools (e.g. Fivetran, Airbyte).
Experience with AI-powered development tools and automated insight generation (e.g. Claude Code).

Responsibilities

Own the dbt models that power Solv’s critical metrics across both internal and customer-facing surfaces. This includes table architecture, field logic, metric definitions, and the modeling choices that determine whether our pipeline meets its SLAs as we constantly add complexity with new products and initiatives.
Set and enforce modeling standards — naming conventions, testing patterns, schema design, documentation. When a metric doesn’t exist or a definition is ambiguous, you define it, build it, and get buy-in.
Diagnose and resolve data issues by tracing lineage across source systems and coordinating with engineering when upstream changes break downstream models.
Own the embedded dashboards and reporting that partners interact with daily inside Solv’s product. Design, build, and iterate on how customers see and use their data.
Manage the full cycle of customer analytics requests: triage what’s a quick fix, what’s a modeling change, and what’s a product gap that needs a different solution.
Design automated analytics deliveries — scheduled reports, data exports, and insight summaries — that reach partners without them needing to log in.
Help evolve our early-stage AI analytics capabilities from internal tools into something that can scale — and eventually reach customers directly in-product.
Build evaluation frameworks and quality guardrails for AI-generated analytics. The bar is higher when output reaches customers — define what “trustworthy enough to ship” looks like.
Keep a pulse on how AI is changing analytics delivery. The tools and approaches are shifting fast; we need someone who evaluates new capabilities with a critical eye, makes smart tradeoff recommendations, and isn’t wedded to one approach just because it’s familiar.