Turn raw data into your competitive advantage
Our analytics background is our superpower. We build data pipelines, dashboards, and analytics infrastructure that give you real-time visibility into what matters most.
What we do
Data analytics is where Stunzer Digital started, and it remains the foundation of everything we do. We've seen firsthand how companies that make decisions based on evidence consistently outperform those running on gut feel. Our mission is to build the infrastructure that makes data-driven decisions the default, not the exception.
We build the complete analytics stack, from data collection and pipeline engineering to transformation, warehousing, and visualization. This isn't about generating pretty charts. It's about building systems that surface the right metrics to the right people at the right time, so your team can act with confidence.
Our data-first approach means we understand the entire lifecycle of your data: where it lives, how it flows, where it breaks, and how to make it trustworthy. We implement data quality checks, automated testing, and governance frameworks that ensure the numbers your leadership team sees are accurate and up to date.
Whether you need a unified analytics platform that consolidates data from dozens of sources, a real-time dashboard that gives your operations team instant visibility, or an automated reporting system that eliminates hours of manual spreadsheet work, we build it to be reliable, scalable, and genuinely useful.
Capabilities
Interactive, real-time dashboards that surface the metrics that matter. No more digging through spreadsheets. Your data, beautifully visualized.
ETL/ELT pipelines that collect, transform, and store your data reliably. From raw sources to analytics-ready datasets.
Go beyond reporting. We help you build a data culture with self-serve analytics, automated reports, and KPI tracking.
Not sure where to start? We'll audit your data landscape, define your strategy, and set up governance frameworks that scale.
Fit
Best fit
Not the right fit
We'll point you in the right direction if any of these apply.
Scenarios
A retail chain with 50+ locations had sales data in their POS system, inventory in a separate platform, and marketing spend in spreadsheets. We built a unified data warehouse that ingests from all sources, transforms the data with dbt, and powers real-time dashboards showing store performance, inventory turns, and marketing ROI, all in one place.
A SaaS company wanted to predict which customers were at risk of churning. We built a customer health scoring system that combines product usage data, support ticket history, billing patterns, and engagement metrics into a single score, surfaced in a dashboard their customer success team checks every morning.
A financial services firm was spending 60+ hours per quarter manually compiling regulatory reports. We built an automated pipeline that pulls data from their core systems, applies the required transformations and validation rules, and generates compliant reports, reducing the process to a few hours of human review.
Technical approach
Our analytics stack is centered on Python and SQL, with dbt as our transformation layer of choice. We model data using star schema and dimensional modeling patterns that make complex queries fast and intuitive. For orchestration, Apache Airflow manages pipeline scheduling, dependency resolution, and failure handling, with alerting so you know immediately when something needs attention.
On the database side, we typically work with PostgreSQL for transactional workloads and analytical queries, scaling to Snowflake or BigQuery when data volumes or concurrency demands warrant it. Every pipeline includes data quality checks using tools like Great Expectations, with automated testing that catches schema changes, null values, and anomalies before they reach your dashboards.
For visualization, we build custom dashboards when the use case demands it (React-based, embedded in your existing tools) or implement Power BI and Metabase when self-service analytics is the priority. Real-time requirements are handled through streaming architectures with change data capture and materialized views, ensuring your team always sees fresh data without sacrificing query performance.
Tech stack
Common use cases
Let's build something
A 30-minute call. We dig into what you're actually trying to do, and you leave with a clearer picture, free.