DGTL Data
One Dashboard Your Entire Team Actually Trusts
Data infrastructure, analytics, and governance built alongside Build, AI, and Advisory so dashboards are trustworthy, AI has clean inputs, and the exec team stops arguing about whose numbers are right. Headquartered in Quito, inside your East Coast team’s working day all year, delivery across the Americas.
Your company generates more data than ever, but your team can't answer basic questions without asking an engineer.
Marketing doesn't know which campaigns drive revenue because attribution lives in five different tools. Sales can't forecast accurately because CRM data is incomplete and nobody trusts it. Product can't measure feature adoption because event tracking was implemented inconsistently. And when your CEO asks for a dashboard, someone exports a CSV and builds it in Google Sheets.
You don't have a data problem. You have a data infrastructure problem. Without a centralized, well-governed data layer, every team builds their own version of the truth, and none of them agree.
Better together
Hiring DGTL for one practice works. Here is why most engagements use more than one.
Data works closely with Build (data architecture embedded into product engineering), AI (data infrastructure that powers agent workflows and RAG systems), Marketing (attribution modeling and marketing analytics), Sales (pipeline analytics and revenue forecasting), and Secure (data governance, access controls, and compliance). When we built the growth engine for our B2B commerce client, Data was the backbone, connecting every marketing touchpoint to closed revenue so the team could see exactly what was driving pipeline.
How we staff Data
Every engagement lands the seniority your scope needs. No bait-and-switch between the sales call and the kickoff. No junior-heavy delivery teams behind a Principal on the deck.
10+ years
Principal Data Engineer
Owns data architecture, warehouse strategy, and governance. Has built a modern data stack from zero in at least one production environment.
6 to 9 years
Senior Analytics Engineer
Fluent in dbt, Snowflake or BigQuery, Fivetran, Airflow. Owns the transformation layer and the BI semantic model.
3 to 5 years
Mid Data Analyst
Ships dashboards, writes SQL, and handles day-to-day analytics requests. Comfortable translating exec questions into queryable definitions.
variable
ML Engineer Specialist
Model deployment, feature engineering, production ML infrastructure. Dropped in when the data work is feeding a live AI system.
DGTL Readiness Index
The Data dimension, before and after.
The Data dimension grades pipeline reliability, warehouse architecture, governance, and self-serve analytics literacy. We move it by replacing the brittle spreadsheet-driven stack most mid-market companies have with a dbt-first warehouse that teams actually trust.
Day 1, typical pre-engagement
2.0 / 5
Day 90, after cross-practice work
3.5 / 5
Median delta across 90-day engagements: +1.5 points. Composite profile from discovery engagements with mid-market companies. See the full 8-dimension framework.
What we do
Every module ships with senior practice leads, clear deliverables, and measurable outcomes. Engagements combine the modules you need into one scoped program.
Data Warehouses
Snowflake, BigQuery, and Redshift builds with sensible cost controls, role-based access, and schemas designed for analyst self-service.
ETL and ELT Pipelines
Fivetran, Airbyte, and dbt pipelines that land clean, tested data into your warehouse on the schedule your business actually needs.
BI and Dashboards
Looker, Metabase, and Tableau dashboards built for executives, operators, and analysts, with one definition per metric.
Customer Data Platforms
Segment, RudderStack, and Hightouch CDP implementations that unify customer data across product, marketing, and sales tools.
Attribution Modeling
Multi-touch attribution and marketing mix modeling that survive the loss of third-party cookies and iOS privacy changes.
Experimentation Platforms
A/B testing platforms and experiment review processes in Statsig, LaunchDarkly, and Eppo, with statistical rigor built in.
Data Governance
Data ownership, access policies, and classification programs aligned to GDPR, LOPDP, and sector-specific requirements.
Data Quality Monitoring
Monte Carlo, Elementary, and custom checks so broken pipelines page the data team instead of surprising the CEO in a board meeting.
Reverse ETL
Hightouch and Census flows that push warehouse data back into Salesforce, HubSpot, and ad platforms for activation.
Real-time Analytics
Kafka, ClickHouse, and Tinybird stacks for sub-second analytics on product events, trading data, and operational signals.
Semantic Layer
Centralized metric definitions in Cube, dbt Semantic Layer, and LookML so every dashboard uses the same math.
Product Analytics
Amplitude, Mixpanel, and PostHog implementations with tracking plans that product managers actually use in weekly reviews.
Event Instrumentation
Tracking plan design and Segment or RudderStack instrumentation, reviewed with product before a single event ships to prod.
Feature Stores
Feast, Tecton, and custom feature stores that let ML teams reuse features across training and serving without drift.
How it works
Data Audit & Maturity Assessment
We inventory your current data sources, evaluate data quality, map existing pipelines and tools, and assess your team's analytics maturity. The output is a prioritized roadmap.
Warehouse & Pipeline Architecture
We design and deploy your central data warehouse, configure ingestion pipelines from all source systems, and build the transformation layer using dbt for version-controlled, tested data models.
Identity Resolution & CDP
We unify customer data across systems into a single profile, so marketing, sales, product, and success teams all see the same customer, not fragments spread across five tools.
Dashboards & Self-Serve Analytics
We build dashboards shaped around how each team actually makes decisions and train your people to explore data independently, reducing the "can you pull this for me?" requests that bottleneck data teams.
Governance & Ongoing Optimization
We implement data quality monitoring, access controls, documentation standards, and alerting for pipeline failures, so your data infrastructure stays reliable as your company grows.
Engagement models
Three ways to buy Data engagements. The difference is what happens at the end.
Same team underneath all three. One MSA, one SOW per engagement. What changes is how long you commit, what flexes once work starts, and who sits on the team. We scope every engagement to what you’re building instead of dropping you into a published tier.
01Project | 02Retainer | 03Embedded Team | |
|---|---|---|---|
| At the end | ProjectIt ends. | RetainerIt keeps going. | Embedded TeamIt renews. |
| Ideal stage | ProjectPre-launch, MVP, redesign, audit, certification | RetainerGrowth phase, ongoing optimization, scaling | Embedded TeamScale phase, fractional executive leadership |
| Commitment | Project4–16 weeks | RetainerMonth-to-month after a 3-month minimum | Embedded TeamQuarterly, renewable |
| How it starts | ProjectPaid discovery sprint (1–2 weeks) | RetainerKickoff + priorities alignment session | Embedded TeamTeam matching + onboarding week |
| Who’s on it | ProjectCross-practice squad assembled for your project | RetainerDedicated team lead + rotating specialists | Embedded TeamFull-time embedded specialists |
| What flexes | ProjectDefined deliverables and timeline | RetainerFlexible within allocated hours | Embedded TeamFlexible within team capacity |
| Where we’d start | We’d start with a Project: stand up the stack and the first dashboards, then grow from there. | ||
Not sure which one fits? Tell us what you’re working on and we’ll recommend the right model based on your goals, timeline, and budget.
We custom-scope every engagement. Most focused engagements start in the five figures, and multi-practice transformations scale into the six and seven figures. Retainers and embedded teams are priced monthly, based on seniority and hours.
See the full comparisonResults
Metrics from recent DGTL Data engagements:
Unified visibility
Single customer view
Marketing, sales, and product data unified for a B2B commerce client
Reporting cycle
Days → real-time
Executive dashboards replaced manual CSV exports
Attribution accuracy
First reliable model
Multi-touch attribution connecting marketing spend to closed revenue
Data team requests
60% reduction
Self-serve dashboards reducing ad hoc engineering requests
What clients say
“DGTL gave us one dashboard everyone looks at now. Sounds simple but it took someone who actually understood how SaaS data flows to make it work.”
COO, Edtech
Representative example. Roles and industries are shown; specific companies and individuals are not identified until signed releases are available.
Our stack
The 15 tools we reach for most. We use plenty of others when the engagement calls for them.
Quito HQ, delivery across the Americas
UTC-5 all year, an hour or less from your East Coast team in either season. Bilingual English and Spanish. No handoff at 5pm, and nothing waits overnight when the US clocks change in March and November.
Why this matters
The problem
Your exec team argues about numbers every Monday, your data lives in six different tools, and pulling a board report takes two engineers and a week. Marketing has one version of the numbers, sales has another, and nobody trusts either. You can't measure what matters because you can't access what you have.
What we do about it
We unify your data into a single warehouse, build reliable pipelines, and create dashboards that everyone trusts.
What you get
Your team makes faster decisions, your AI features have a solid foundation, and your board gets the metrics they need without an engineering sprint.
Frequently asked questions
Ready to build your data foundation?
Tell us about your data challenges. We’ll design the infrastructure to solve them.