Data Engineering · 01 of 05

Decisions, not dashboards.

A semantic layer your whole org can trust, dashboards that answer the next question, and embedded analytics that earn product real estate. We turn data platforms into decision systems — one metric definition, everywhere.

Scope an analytics build ↗See the stack
1Metric definition · everywhere
−68%Ad-hoc Slack data asks
4 wkTo self-serve in production
Revenue · YTDUpdated 14s ago
Revenue
$48.2M
+12.4%
ARR
$58.7M
+8.1%
Net new
$2.1M
+22%
Churn
2.4%
+0.3 pts
Pipeline trend · last 12 weeks
By segment
Win rate62%
Metric: arr_totalSource: dbt · martsOwner: @finance-data
What you get

Six surfaces, one source of truth.

Self-serve, executive, embedded, real-time — all reading from one governed metric layer. No more "whose number is right?"

Semantic layer & metrics

One definition of arr, churn, cohort_ltv — consumed by every tool from Slack to Tableau. No more 'whose number is right?'

dbt metrics · Cube · LookML

Executive scorecards

One-page weekly view your CEO actually reads. Decisions, not 17 tabs — with rolling-window narratives generated from the data.

scorecard · narrative · drilldown

Self-serve BI

Looker / Hex / Metabase set up so non-technical teams can answer their own questions — without dialing the data team into every standup.

governed · explorable

Embedded analytics

Customer-facing dashboards inside your SaaS product. Multi-tenant, row-scoped, brand-aligned, and fast at 99th percentile.

multi-tenant · row-scoped

Real-time dashboards

Sub-second freshness for ops — trades, dispatch, gameday. Streaming inputs through ClickHouse, Materialize, Tinybird.

sub-second · streaming

Governance & lineage

Every chart traces back to a column. Owners, freshness SLAs, deprecation flow. Auditors love it; analysts love it more.

lineage · SLA · ownership
How it works

A semantic spine between your warehouse and your tools.

Define a metric once. Consume it everywhere. The same number in Tableau, in Slack, in your CEO's phone widget.

01 · SourcesWarehouseLakehouseStream02 · SemanticMetricsJoins · DimsAccess & SLA03 · SurfacesBI toolsEmbeddedSlack · EmailAI agentsCross-cuttingLineageQualityAccess RBACObservabilityCatalog · ownership · freshness · cost

Pre-warehouse work is yours. Everything after is ours.

We plug into whatever sits underneath — Snowflake, BigQuery, Databricks, Redshift — and build the layer that makes it useful to humans.

  • 01
    Pick the canonical metrics

    20-ish numbers that move the business. We agree definitions in writing before a single chart is drawn.

  • 02
    Build the semantic layer

    dbt metrics or Cube as the contract. Every BI tool reads from there — no copy-paste SQL.

  • 03
    Design surfaces for audience

    Exec scorecard, ops live view, embedded customer dashboard — same metrics, fit to the user.

  • 04
    Govern & iterate

    Catalog, ownership, freshness SLAs. New metrics go through review, not Slack DM.

Tech stack

Tool-agnostic, opinion-rich.

We'll meet you on the BI stack you have — and tell you, candidly, where it's holding you back.

Semantic / metrics

dbt SemanticCubeLookMLMetricFlow

BI / exploration

LookerTableauPower BIHexModeMetabase

Embedded

SigmaEmbeddableCube + RechartsApache ECharts

Real-time

ClickHouseMaterializeTinybirdPinotDruid

Warehouses

SnowflakeBigQueryDatabricks SQLRedshift

Catalog & quality

AtlanCollateMonte CarloGreat Expectations

Activation

HightouchCensusSlack alerts

Visualization libs

ObservableD3Vega-LitePlotly
From vision to victory

From request-queue to self-serve, in five steps.

A six-week path to retire the ad-hoc backlog and make data useful to the people closest to the work.

01
Week 1
Metric inventory

The 20 numbers that matter, owners assigned, definitions written.

02
Week 2–3
Semantic layer

Ship metrics in dbt / Cube. Wire one BI tool as the canonical reader.

03
Week 3–4
Build flagship dashboards

Exec, revenue, ops. Designed for the audience, not for the data team.

04
Week 5
Enable self-serve

Train, document, set up review flow. Retire 60% of ad-hoc tickets.

05
Ongoing
Govern & expand

Monthly catalog review, new metrics through a tiny intake form.

Where it lands

Three analytics builds we've shipped.

Less 'dashboard refresh,' more 'the report nobody had to ask for.'

Pattern · SaaS · Embedded

Analytics shipped inside the product.

Multi-tenant embedded dashboards for a HRIS platform. Row-scoped, theme-aware, sub-second on 100M-row tables.

p95 280msQuery latency
+14% NRRTenants on premium
CubeClickHouseRecharts
Pattern · Retail · Executive

A scorecard the CEO reads on the train.

One-page weekly view replacing 14 dashboards. Narratives auto-generated from the underlying data with anomaly callouts.

1 pgReplaced 14 dashboards
−72%Ad-hoc data asks
dbt metricsLookerHex
Pattern · Ops · Real-time

Dispatch floor at sub-second freshness.

Live view of fleet, routes and exceptions for a logistics ops floor. Streaming from Kafka through Materialize.

< 1sEnd-to-end
4 citiesLive
KafkaMaterializeTinybird
Why ETY

Analytics engineers who've owned the metric.

18Semantic layers in production across SaaS, retail, fintech and logistics.
−68%Median reduction in ad-hoc data tickets after rollout.
p95 280msMedian query latency on operated BI deployments.
Tool-agnosticWe work in whichever BI tool your team already loves.
Continue exploring
ETL & Data Warehousing

The structured, modeled warehouse this analytics layer reads from.

→
ELT & Data Lakehousing

When you need to keep the raw data and let multiple engines read it.

→

One metric. Defined once. Used everywhere.

Send us your three most-disputed numbers. We'll come back with a metric contract, a semantic-layer plan, and the dashboards that retire the Slack debate.

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