Your data teams are fixing pipelines. They should be analyzing data. We build data engineering infrastructure that works.
Six sources pulling the same revenue number in four different directions. Analysts spending Monday morning reconciling last week's reports instead of reading them. We build the ETL pipelines, data warehouse, and dbt models your team needs to trust every number on every dashboard.
60% of analyst time goes to fixing pipelines. Zero percent of that time moves the business.
"Our marketing team opened one report and saw $2.4M revenue. Our finance team opened another and saw $1.9M. Both pulled from the same system. Neither team could trust a number before the board meeting."
What we hear from CTOs before the engagement starts
Six stages. Six deliverables. One system your team owns. Select any stage to see exactly what we build and what we hand you.
Each stage of your data pipeline is a distinct discipline with its own failure modes and quality standards. We build all six together, designed from the start to connect cleanly. No handoffs between contractors. No undocumented code your team cannot read.
Before a single pipeline runs, we map every data source your business generates: transactional databases, SaaS APIs, event streams, flat files, and third-party integrations. The source audit documents schemas, flags quality problems, and classifies each source by ingestion method. Your team sees exactly what data exists and where problems are — before we write a line of code.
- Source inventory document: every system, schema, and data type
- Schema documentation per source with field-level notes
- Data quality baseline: known gaps, nulls, and anomalies flagged before build
Data moves from your source systems into a controlled landing zone reliably, at any scale, with full visibility into what ran and what failed. Batch jobs use incremental loading so we only move new or changed rows. Real-time sources use change data capture or event streaming. Every pipeline has retry logic, failure alerting, and dead-letter queues so broken jobs surface immediately — not three days later.
- Batch and streaming ingestion pipelines for every connected source
- Incremental load strategies per source (no full reloads)
- Alerting and monitoring: you know within minutes when a pipeline fails
Raw ingested data becomes clean, tested, and documented business models. dbt turns SQL scripts into versioned, testable models with automatic data lineage. Every metric definition lives in code. Business logic is documented inside the model, not in a wiki nobody reads. Your next analyst inherits a codebase, not a mystery.
- dbt project with all business metrics in version-controlled SQL
- Data tests and quality assertions at the model layer
- Auto-generated lineage docs: every metric traced to its source
The warehouse is the single place every report comes from. We design schemas around your query patterns, not around a database textbook. Star schema, wide tables, or materialized aggregations — the choice is driven by how your analysts actually run queries, not by convention. Partitioning and clustering reduce query costs from the start.
- Warehouse schema design document, reviewed with your team before build
- Partitioning and clustering strategy to control query costs
- Cost governance plan: your warehouse bill stays predictable
The serving layer makes warehouse data fast and accessible to every team that needs it: business intelligence tools, internal apps, and data science workflows. Materialized views and a semantic layer mean your dashboard loads in seconds instead of querying the raw warehouse each time. Role-based access controls decide who sees what — and your security team can audit it.
- Semantic data model in dbt or Cube — one place where metrics are defined
- Materialized views for fast BI queries without raw warehouse scans
- Role-based access governance: right data to the right person
This is where your team works every day. We connect the warehouse to your BI tools, build the first five core dashboards to confirm data quality end-to-end, and hand over a self-serve environment your analysts can extend. Adding a new report means writing SQL in dbt. No pipeline tickets. No waiting for us.
- BI tool setup and first 5 core dashboards — revenue, marketing, operations, retention, and one you choose
- Self-serve analytics training for your data team
- Complete data dictionary: every metric defined, every source traced
Six capabilities that move your data team from pipeline maintenance to business analysis.
Every pipeline is tested, documented, and version-controlled before we hand it over. Batch and streaming sources. Incremental loading patterns. Retry logic and alerting built in from the start. Your next engineer can read every line.
Revenue, conversion rate, churn, and customer lifetime value mean the same thing in every dashboard that uses them. Every metric is defined once in dbt, in version-controlled SQL. No competing numbers. No "whose number is right" conversation before a board meeting.
Operational events, orders, and clickstream data land in your warehouse within seconds of occurring. We design streaming architectures using Kafka and Pub/Sub, scaled to your event volume. Your team gets real-time data without building real-time infrastructure.
Data quality tests run at ingestion, transformation, and serving layers. Broken pipelines fail loudly before bad data reaches any dashboard. Your data team sees the problem first. Your stakeholders never know it happened.
Every dataset documented. Every metric traceable to its source table. Role-based access controls keep finance data with finance and HR data with HR. Governance that survives the next round of team turnover, not just the current quarter.
BI tool setup, five core dashboards, and a self-serve layer your team can extend. Your analysts add new reports by writing SQL in dbt. No pipeline tickets. No waiting for a vendor to schedule a call.
Your data infrastructure is solid, but your reports need work. See Business Intelligence and Analytics Development.
See Business Intelligence and Analytics Development →Four conflicting dashboards became one. Leadership now walks into every board meeting trusting the numbers.
A direct-to-consumer ecommerce brand ran analytics across their CRM, marketing platform, and ERP as three separate systems. Each system defined customer revenue and marketing attribution differently. Reports from different teams contradicted each other every week. Before every leadership meeting, analysts spent hours manually reconciling the numbers — with no guarantee the final figure was right.
- Revenue defined differently in CRM, marketing platform, and ERP
- Manual reconciliation before every leadership meeting
- Analysts fixing data every Monday instead of reading it
We built a centralized BigQuery data warehouse with a star schema tuned for their analytics queries. Incremental ETL pipelines pulled data from their CRM, marketing tools, and ERP. dbt models standardized every metric definition. Revenue, customer lifetime value, and marketing attribution now have one definition — and that definition runs every dashboard in the company.
- Leadership now runs every board meeting from one shared dashboard
- BigQuery warehouse with star schema, all metrics governed in dbt
- Incremental ETL from CRM, marketing, and ERP — no manual reconciliation
The tools that build your pipeline. Select any tool to see how we use it and why.
The warehouse we recommend most often. BigQuery's serverless model means no cluster provisioning — you pay per query, not per hour of server uptime. Storage and compute are separate, so a slow query does not inflate your storage bill. Works natively with dbt, Looker, and the rest of the GCP ecosystem. Best fit: teams on Google Cloud or those who want predictable, query-based cost control.
Our choice for multi-cloud teams and organizations sharing data across business units or external partners. You provision virtual warehouses sized to each workload — an analytics warehouse, a data science warehouse, a reporting warehouse — and scale each independently. Best fit: enterprise teams with complex data sharing needs or existing Snowflake contracts.
Every metric your business uses is defined once in dbt, in SQL your team can read and change. dbt turns raw SQL queries into versioned, tested, documented models with automatic data lineage. Your analysts can modify a metric definition without filing a pipeline ticket. Your next data hire inherits documented code, not mystery logic.
Pipeline orchestration for teams with complex scheduling and retry requirements. Airflow schedules every pipeline job, retries failures automatically, and tells you exactly which step broke and why. We deploy on Cloud Composer (managed) or self-hosted depending on your infrastructure setup. No more silent pipeline failures at 2am.
Real-time event streaming for high-volume, time-sensitive data. Orders, clickstream events, and operational metrics arrive in your warehouse within seconds of occurring. We design Kafka topics, consumer groups, and schema registries scaled to your specific event volume. Your dashboards reflect what is happening now, not what happened last night.
For batch processing at a scale that a single machine cannot handle. Historical data backfills, joins across billions of rows, and machine learning feature prep. We use Spark when the data volume justifies it, not as a default — because most teams do not need it and pay a significant complexity cost when they add it unnecessarily.
Managed connectors for the 300+ SaaS tools most data teams need to ingest. Salesforce, HubSpot, Stripe, and Google Ads connect without custom code. We use managed connectors when they exist and build custom ingestion scripts when they do not — you get the right tool for each source, not a single-vendor answer.
Custom ingestion and transformation scripts for sources that no connector covers. API integrations, change data capture implementations, schema transformation logic, and data validation scripts. Every Python pipeline is tested, version-controlled in Git, and documented so your team can modify it without calling us.
Five things most data engineering vendors get wrong. What we do instead.
What data and engineering leaders ask before starting.
Every engagement starts with a data audit and architecture design. From there, we build ETL or ELT pipelines, set up your data warehouse, build dbt models for your key metrics, add data quality testing at every layer, and produce documentation your team can use. The discovery phase comes first and produces the architecture plan before any pipeline work begins — so you see the design before you commit to the full build.
We build on Google BigQuery, Snowflake, Amazon Redshift, and Databricks. Platform selection is based on your cloud provider, query patterns, team SQL skills, and cost constraints. We have no commercial partnerships with any platform vendor. The recommendation we make is the one that fits your situation, not the one that benefits us. See engagement models for how we scope the platform decision.
A single-domain data warehouse build typically takes 6 to 10 weeks. Builds covering multiple business domains, such as sales, marketing, and operations together, run 12 to 20 weeks. We phase the build so you get a working, queryable layer at the end of each phase — not one large delivery at the end of the project.
dbt is a transformation tool that lets your team write SQL data models with version control, testing, documentation, and automatic data lineage. Most teams with more than one analyst and more than three data sources benefit from dbt. We build dbt into the transformation layer so your data team inherits a maintainable codebase rather than raw SQL scripts scattered across notebooks.
Engagements typically run $40,000 to $250,000. At the low end: fewer sources, one business domain, batch-only ingestion, standard warehouse setup. At the high end: many sources, streaming requirements, multi-domain warehouse, data quality governance, and training included. We scope before we quote. The discovery phase is priced separately and produces the architecture plan that sets the full project cost. See data engineering pricing for the full breakdown.
We turn away work that is not the right fit. Here is how we define fit.
Tell us your data situation. We will be direct about whether data engineering is the right starting point — or whether something else fits better.
Describe your data problem. We will tell you what it costs to fix it.
We review every submission and respond within two business days. No commitment. No pitch.
Expect a reply within two business days. We will review your requirements and send an architecture proposal within three days of our first call.
