EGGKNITE is a data & analytics engineering agency working with Denver brands as a senior, remote team. We help the Denver and Front Range area companies tie every dollar of spend to revenue — without the overhead of a local retainer shop.
Denver blends B2B SaaS with a strong outdoor, wellness and consumer-brand culture. Growth teams here run lean, so the leverage is systems — lifecycle and automation that compound without new headcount.
Denver blends B2B SaaS with a strong outdoor, wellness and consumer-brand culture. Growth teams here run lean, so the leverage is systems — lifecycle and automation that compound without new headcount. That is the context we build data & analytics engineering for.
We run as a distributed senior team, not a Denver storefront — so your budget buys operators and engineering instead of a downtown office. We work inside your own accounts, on MT hours, and most clients never needed us in the room to hit their numbers.
We go far beyond standard analytics by discovering deeper customer signals and behavioral patterns that unlock significantly higher business returns. Our advanced targeting frameworks feed platforms enriched, high-intent signals most competitors never surface - driving more precise acquisition, stronger retention, and greater overall profitability.
Trustworthy tracking feeding one customer profile your whole stack agrees on. We implement GA4, GTM, and server-side tracking that survives ITP and ad blockers, validated event by event with consent wired in. A CDP then stitches accounts, people, and devices into one deterministic-first profile every tool in your stack reads.
A spend-to-revenue picture that holds up in a finance review. We run multi-touch attribution where the data supports it, media mix modeling where it does not, and incrementality tests to settle the argument when the two disagree. The result holds up in a full finance review, so you stop debating which channel gets credit and start reallocating budget on numbers everyone trusts.
Enriched profiles synced to the channels that can act on them. We layer firmographic, technographic, intent, and behavioral signals onto every profile, powered by MadSight, our data activation platform. Enriched segments flow to Meta, Google, LinkedIn, and your CRM on a continuous schedule, so your platforms bid on high-intent audiences most competitors never surface.
Modeled data you own, forward-looking models, and dashboards that tie spend to revenue. Modeled, tested, version-controlled tables in your own warehouse, with reverse ETL back to the tools your teams work in, one source of truth every report and model reads. On top run LTV, churn, and demand forecasts with scenario planning, and executive dashboards that reconcile spend to revenue automatically, so budgets are set on evidence, not guesswork.
Denver's market over-indexes on B2B SaaS, outdoor & consumer brands and wellness. The data & analytics engineering playbook is not identical across them, so we tailor the audiences, offers and measurement to the sector you actually sell into.
We follow every tag, event, and table from first click to closed revenue and write down where the trail breaks. You get a line-by-line account of what is missing, double-counted, or quietly unused, ranked by dollars at stake rather than ease of fixing. Nothing gets built until we agree on what is actually wrong.
A tracking plan, event taxonomy, and identity spec designed around your funnel, your channels, and your consent obligations. We settle naming, ownership, and definitions on paper before a single tag ships. Everyone signs off on one spec, so there is one thing to build against.
GA4, GTM, server-side collection, the CDP, and warehouse pipelines all built precisely to the blueprint. Every event is checked in staging, confirmed in production, and documented the moment it goes live. Clean input up front means the models and dashboards downstream have something solid to stand on.
Enriched audiences flow automatically into every activation channel, dashboards refresh in real time, and the entire system is fully documented inside your own data stack.
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