Your product generates data. Your investors want dashboards. Your product team needs analytics to prioritize. But your data lives in 12 different systems, your reports take 3 hours to build, and nobody trusts the numbers. We build the data infrastructure that turns raw data into reliable decisions.
Customer data in Stripe, usage data in Mixpanel, product data in PostgreSQL, marketing data in HubSpot. When your CEO asks “how many active users do we have?” and three people give three different numbers, your data infrastructure is broken.
If your analyst spends 80% of their time wrangling data and 20% analyzing it, you have a pipeline problem, not a people problem. We build the infrastructure that makes analytics self-serve.
You don’t need a data lake, a machine learning platform, and a team of 10 data scientists. You need clean data, reliable pipelines, and dashboards that answer the questions your business actually asks. We start simple and scale.
We don’t build data infrastructure for its own sake. Every pipeline, every dashboard, every model we build answers a specific business question or automates a specific decision.
Our engagements start with a Data Assessment — we map your data sources, identify gaps, and define the 5–10 questions your business needs data to answer. Then we build the simplest architecture that answers those questions reliably.
We favor modern, managed tools (dbt, Snowflake, Fivetran, Looker) over complex custom builds — because your data team should spend their time on analysis, not infrastructure maintenance.
We build the plumbing that moves data from source to warehouse to dashboard — reliably, on schedule, and with proper monitoring. Our pipelines handle schema changes, late-arriving data, and source system failures gracefully.
Common deliverables: ETL/ELT pipelines, data warehouse design, CDC (change data capture) systems, streaming data pipelines, data quality monitoring.
Before building anything, we help you define what data you need, how it should be organized, who owns it, and how it stays accurate. This prevents the “nobody trusts the numbers” problem that plagues most growing companies.
Common deliverables: Data strategy documents, data catalog design, data quality frameworks, access control policies, data lineage mapping.
We build dashboards that people actually use — not 50-chart monstrosities that nobody opens after launch week. Clean, focused views that answer the specific questions each stakeholder needs answered.
Common deliverables: Executive dashboards, product analytics dashboards, financial reporting, customer health dashboards, self-serve analytics environments.
When historical analysis isn’t enough, we build models that forecast what happens next: churn prediction, revenue forecasting, lead scoring, demand planning, and anomaly detection.
Common deliverables: Churn prediction models, LTV forecasting, lead/opportunity scoring, demand forecasting, anomaly detection systems.
For products that need instant insights — real-time dashboards, live alerting, streaming aggregations, and event-driven analytics.
Common deliverables: Real-time dashboards, streaming data pipelines (Kafka, Kinesis), live alerting systems, event aggregation, real-time feature computation.
Snowflake, BigQuery, Redshift, PostgreSQL, ClickHouse
Dbt, Apache Spark, Pandas, SQL
Fivetran, Airbyte, Stitch, custom connectors, Apache Kafka, Amazon Kinesis
Looker, Metabase, Tableau, Power BI, Superset
Apache Airflow, Dagster, Prefect
Great Expectations, dbt tests, Monte Carlo
If you have more than 2–3 data sources and more than one person asking questions about your data, yes. A data warehouse provides a single source of truth, dramatically faster queries, and the foundation for self-serve analytics. Modern warehouses like Snowflake and BigQuery are affordable even for small companies.
For most growing companies, the answer is “buy the infrastructure, build the logic.” We use managed tools like Snowflake, Fivetran, and dbt because they eliminate maintenance overhead. Your custom business logic — the transformations, metrics definitions, and models — is where the engineering investment should go.
A single dedicated data engineer can support a company of 50–200 people if the architecture is well-designed. We typically start with 1 data engineer, add a second as your data sources and analytics needs grow, and layer in analytics engineering as the organization matures.
Yes. We’re tool-agnostic and can work within your existing data stack. If your current tools are the right choice, we’ll improve how they’re used. If they’re limiting you, we’ll recommend alternatives with a clear migration path.
Data Assessments start at $5,000 as a standalone deliverable. Dedicated data engineers are $3,500–$5,500/month. Foundation builds (warehouse + initial pipelines + dashboards) typically run $20,000–$50,000.
Tell us what questions your business can’t answer today. We’ll show you the shortest path from raw data to reliable decisions.
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