Senior Business Intelligence Engineer

Job Description

Team: Data & Analytics

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Reports to: Head of Data

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Location: Remote/hybrid, Pacific Northwest preferred

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Type: Full-time, exempt.

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Managing one contract data engineer

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Base salary: $115,000–150,000 annually, plus bonus potential

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This role is two parts:

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1. Backend data engineering in dbt and the Snowflake warehouse
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2. Building dashboards and doing analysis in Sigma (or ad hoc), as requested by stakeholders
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How to apply

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In your resume, include a few sentences about a data product you shipped: who used it, what was wrong with the first version, and what you changed after they told you — that last part is what we’re most interested in.

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About GLDN

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GLDN is a high-growth, direct-to-consumer jewelry brand that blends craft and technology to bring delight and connection through meaningful, personalized pieces. We design, manufacture, and ship from our headquarters in La Conner, Washington, serving hundreds of thousands of customers each year across a catalog of 20,000+ SKUs spanning made-to-order and in-stock products. We rely on data to drive smart decisions across our entire operation — from marketing and ecommerce to manufacturing and fulfillment — and as we grow, the data infrastructure that powers those decisions needs to grow with us.

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Role summary

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The Senior BI Engineer is the hands-on builder and technical owner of GLDN’s data foundation. They are the person who sits closest to our enterprise data. It’s a pivotal role for someone curious, driven by insights, and passionate about building: writing the pipeline, designing the model, shaping the dashboard, and rolling it out. They own the full data lifecycle end to end, rather than handing off to specialists.

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They operate with the confidence to put an imperfect version of a data product in front of real stakeholders, listen to what doesn’t work, and rebuild fast. They are self-directed enough to turn a loosely defined problem into a reliable pipeline without a detailed spec, and close enough to the data to notice when a number is quietly wrong. This role is collaborative and approachable at every level, from the studio floor to the founder.

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Why this role is interesting

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Most data roles are narrow. This one spans the whole business, including marketing, ecommerce, manufacturing, and supply chain, because GLDN manufactures locally in Washington and ships globally.

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We sell made-to-order, personalized jewelry, so digital demand turns into physical work with real capacity limits, and personalization creates many-to-many product/component relationships that complicate ordering, forecasting, and reporting. We spent the last year standing up our data platform- Snowflake, dbt, Fivetran, and Sigma. This role takes it to the next stage: a well-modeled, tested layer the whole business and AI can query with confidence. It’s built by the person closest to the data rather than assembled ad hoc by business teams.

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Some of what’s waiting for you:

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• Accurate daily sales and revenue, even through cancellations, production delays, and returns

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• Product reporting and insights, an area we’re still building out

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• Marketing and customer insights, real acquisition drivers vs. what’s just taking credit

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• Demand forecasting and replenishment, alongside our Planning team

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• Returns, what’s coming back, from where, and why

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• The cost of being out of stock, measured rather than estimated

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• Gold prices, pricing, and margin visibility as metal prices move

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• Labor cost by product, since nearly everything is handmade

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• A platform that keeps scaling cleanly as sources and models grow

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What you’ll do

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• Own the pipelines and the models: Ingestion (Fivetran and custom), transformation (dbt), and modeling (Snowflake) across ecommerce and manufacturing. This includes orchestration, observability, and data quality, so you hear about a broken pipeline before Operations does.

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• Build the reports too, and support Sigma users: Design and ship dashboards and models yourself in Sigma rather than spec’ing them for someone else. Roughly half the value here comes from that side. You’re also the go-to for Sigma users across the business, which doubles as your best signal for what to build next.

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• Ship early, then fix it: Put a rough version in front of whoever asked, listen to what’s wrong, and rebuild. We’d rather see v1 in a week and revise twice than v3 in two months. The one exception is data integrity- wrong numbers are never fine, deadline or not.

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• Go look at the data, and fix things at the source: When a number looks off, open the raw tables, and trace problems upstream to whatever created them rather than patching downstream indefinitely.

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• Make the data AI-accessible, safely: Build certified, tested marts that LLMs and agents can query directly, not raw tables, since a confident wrong answer does more damage than no answer. Use AI coding tools (Cursor, Claude Code, Copilot) on your own work too.

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• Be the technical owner: Principal owner of Snowflake, Fivetran, dbt, and Sigma: set standards for modeling, documentation, testing, and governance, review code properly, mentor analysts and analytics engineers, and directly manage a contract data engineer.

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• Own the data budget: The stack’s spend is yours, along with the value it delivers. Unused pipelines, oversized warehouses, and unnecessary full rebuilds are a design problem, not somebody else’s.

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• Partner with the business: Marketing, Operations, Planning, Finance, Product, and up through leadership and our founder- turning fuzzy questions like “why are returns up on this collection?” into real insight.

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What good looks like a year in:

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• Daily and monthly demand and revenue reporting that finance and leadership stop double-checking

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• Product and launch performance answered in days, not one-off spreadsheets

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• Returns understood well enough to act on — which products, which customers, what changes

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• Margin visibility that keeps pace with metal prices and pricing decisions

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• Data input migrated out of Sigma and into our PLM

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• Planning and forecasting supported well enough to avoid stockouts on key items and pull back as mature items slow

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• Dashboards and models teams actually open every day and week

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• Data quality issues surfaced by monitoring before anyone downstream notices

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• A governed layer business users and AI tools can query without hitting a wrong number

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What you’ll need:

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Required

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• 5+ years in data engineering or something closely adjacent, with real ownership of production systems

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• Strong SQL and Python, and fluency in modern data modeling

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• Genuine hands-on coverage across the full pipeline — ingestion, transformation, modeling, orchestration, and visualization/BI — not depth at just one end

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• Experience architecting a dbt project, not just adding models to one

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• A modern stack comparable to Snowflake, Fivetran, dbt, and a BI layer like Sigma

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• A track record of shipping data products directly to stakeholders and iterating on their feedback

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• Demonstrated use of AI coding tools (Cursor, Claude Code, GitHub Copilot, or similar) in production

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• Hands-on cloud infrastructure experience (AWS or equivalent) and a real feel for cost — warehouse sizing, sync frequency, incremental vs. full rebuilds, storage that’s outlived its use

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• Experience directing a contract or offshore engineer: scoping work, reviewing code, holding the quality bar

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Preferred

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• Ecommerce and marketing data and integrations: Shopify Plus, Attentive, Google Analytics, Triple Whale, Meta, Google

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• Manufacturing, inventory, and warehouse data; Odoo ERP experience a plus• Made-to-order or long-lead-time fulfillment data, and returns-heavy businesses

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• Data orchestration and observability tooling• Data governance and master data management

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What this role doesn’t own

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• Business systems architecture and support for Odoo, Shopify Plus, and ShipStation: that’s the Business Systems team, a close partner

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• Vendor contracts and adding new vendors: you own the data budget’s spend, but expanding it is a conversation with your manager

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• Building a department: you’ll manage one contract engineer and direct analysts technically, but this role is hands-on first, not a management track

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Location and travel

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Remote/hybrid, open to strong candidates across the US with a preference for the Pacific Northwest. Skagit County time isn’t flexible, though: we’ll need you here for onboarding, in-person collaboration, and periodic touchpoints after that — a lot of what makes our data complicated happens on the studio floor, and it’s easier to understand in person than over Google.

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Compensation and benefits

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• Base salary: 115,000–150,000 per year, depending on experience

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• Bonus: Annual performance bonus potential

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• Health: Medical with HSA option (employer-subsidized), plus dental and vision

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• Retirement: 401(k)

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• Time off: Flexible PTO plus paid holidays

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• Family: Paid parental leave

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• Perks: Employee jewelry discount

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How to apply

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In your resume, include a few sentences about a data product you shipped: who used it, what was wrong with the first version, and what you changed after they told you — that last part is what we’re most interested in.

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GLDN is an equal opportunity employer. We’re building a team that reflects the customers we serve, and we consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic.