Data Engineering Company. Decisions Run on Numbers You Can Trust.
Your business generates data from dozens of sources. Today those sources disagree with each other. We build the pipelines, warehouse, and data models that turn scattered events into one version of the truth.
Submit brief → call within 48 hours → architecture proposal in 3 days → pipeline build starts week 2
Your data team spends most of its time fixing data. Not using it.
"Our marketing team opened one report and saw $2.4M revenue. Our finance team opened another and saw $1.9M. Both were pulling from our own data. Neither team trusted their numbers before a board meeting."
What we hear from CTOs before a data engineering engagement starts
Six stages. Every one of them is a deliverable. Click any stage to see what we build there.
Each stage of your data pipeline is a distinct engineering discipline. We build all six as a unified system, not six separate projects handed off between contractors.
We inventory every data source your business generates: transactional databases, SaaS APIs, event streams, flat files, and third-party integrations. The source audit maps schemas, identifies quality issues, and classifies each source by ingestion method before any pipeline code is written.
- Complete source inventory document
- Schema documentation per source
- Data quality baseline assessment
The ingestion layer moves data from source systems into a landing zone reliably, at scale, and with full observability. Batch jobs use incremental loading patterns. Real-time sources use change data capture or event streaming. Every pipeline has retry logic, alerting, and dead-letter queues.
- Batch and streaming ingestion pipelines
- Incremental load strategies per source
- Pipeline monitoring and alerting
Raw ingested data becomes clean, tested, documented business models. dbt transforms SQL scripts into versioned, testable models with data lineage. Every metric definition lives in code. Business logic is documented in the model itself, not in a wiki that nobody reads.
- dbt project with all business metrics
- Data tests and quality assertions
- Auto-generated data lineage docs
The warehouse is the single source of truth for every report your company produces. We design schemas for your query patterns, not for a textbook. Star schema, wide tables, or materialized aggregations: architecture is driven by how your analysts actually query.
- Warehouse schema design document
- Partitioning and clustering strategy
- Cost governance and query optimization
The serving layer makes warehouse data fast and accessible to every consumer: business intelligence tools, applications, and data science workflows. Materialized views, semantic layers, and API endpoints so that your dashboard loads in seconds, not minutes. Role-based access controls ensure the right people see the right data.
- Semantic data model in dbt or Cube
- Materialized views for fast business intelligence queries
- Role-based access governance
The final layer is where your team actually works. We connect the warehouse to your business intelligence tools, build the first set of dashboards to prove data quality, and hand over a self-serve environment your analysts own. Your team can add new reports without touching pipeline code.
- Business intelligence tool setup and first 5 core dashboards
- Self-serve analytics training for your team
- Complete data dictionary for all metrics
Six capabilities your data team gains when you hire data engineering support.
Batch and streaming pipelines from every source your business uses. Incremental loading patterns. Full retry and alerting logic. Pipelines tested and documented before handover.
Every business metric defined once in code. Revenue, conversion rate, churn, and customer lifetime value mean the same thing in every dashboard that uses them. No more competing numbers.
When your business decisions depend on near real-time data, we design streaming architectures using Kafka, Pub/Sub, and Flink. Operational events land in your warehouse within seconds of occurring.
Data tests run at ingestion, transformation, and serving layers. Broken pipelines fail loudly before bad data reaches your dashboards. Your data team sees quality issues before your stakeholders do.
Every dataset documented. Every metric traceable back to source. Role-based access controls prevent the wrong people from seeing sensitive data. Governance that survives team turnover.
Business intelligence tool setup, first 5 core dashboards, and a self-serve environment your team can extend without touching pipeline code. We hand over tools your data team can maintain and grow independently.
Need specific business intelligence and analytics development beyond the data engineering layer?
See Business Intelligence and Analytics Development →From four conflicting dashboards to one source of truth. One analytics layer every team trusts.
An ecommerce and direct-to-consumer business ran their analytics across customer relationship management, marketing platforms, and ERP systems independently. Each system had its own definition of customer revenue and marketing attribution. Reports produced by different teams contradicted each other. Manual reconciliation consumed hours of analyst time every week, and leadership had no confidence in any single number before key business reviews.
- Revenue metric defined differently in three tools
- Manual reconciliation required before every leadership meeting
- Analyst time spent fixing data instead of analyzing it
A centralized BigQuery data warehouse with a star schema optimized for analytics workloads. Incremental ingestion pipelines built using SQL and Python to consolidate data from customer relationship management, marketing platforms, and ERP systems. dbt models standardized all metric definitions. A single version of revenue, customer, and attribution now serves every dashboard across the business.
- BigQuery warehouse with star schema, governed in dbt
- Incremental ETL pipelines from customer relationship management, marketing, and ERP
- Leadership now runs board meetings from one shared dashboard
From ingestion to insight. Click any tool to see where it fits in your pipeline.
Our primary warehouse platform for most engagements. Serverless execution model eliminates cluster management. Separation of storage and compute controls costs. Native integration with Looker, dbt, and GCP services. Best for teams already on Google Cloud or those who want per-query cost control.
Preferred for multi-cloud teams and organizations with complex sharing requirements. Virtual warehouse scaling model lets you provision exactly the compute you need per workload. Strong data sharing and marketplace features. Best for enterprise teams and those already committed to Snowflake licenses.
The transformation layer of every modern data stack. dbt turns SQL queries into versioned, tested, and documented data models. Every metric is defined once. Data lineage is automatic. Your team can modify models without touching pipeline code.
Workflow orchestration for complex pipeline DAGs. Schedules, retries, and monitors every pipeline job. Full observability into which step failed and why. We deploy on Cloud Composer or self-managed depending on your infrastructure preference.
Real-time event streaming for high-velocity data sources. Orders, clickstream events, and operational metrics stream into the warehouse with sub-minute latency. We design Kafka topics, consumer groups, and schema registry to scale with your event volume.
For large-scale batch processing that exceeds single-machine capacity. Historical data backfills, complex joins across billions of rows, and machine learning feature engineering at scale. We use Spark when the data volume justifies it, not as a default.
Managed connectors for standard SaaS data sources. 300+ pre-built connectors handle Salesforce, HubSpot, Stripe, Google Ads, and similar sources without custom code. We use managed connectors where they exist and build custom ingestion where they do not.
Custom ingestion and transformation scripts for sources that no managed connector handles. API scrapers, custom CDC implementations, schema transformation logic, and data quality scripts. All Python pipelines are tested, versioned in Git, and documented for your team to maintain.
Seven things most data engineering agency vendors get wrong. What we do instead.
What engineering leads ask before a data engagement.
A data engineering engagement typically includes a data audit and architecture design, ETL or ELT pipeline development, data warehouse or lakehouse setup, data modeling using dbt or SQL, data quality testing, and documentation. Every engagement starts with a discovery phase that maps your data sources, identifies quality issues, and produces a data architecture plan before any pipeline work begins.
We design and build on Google BigQuery, Snowflake, Amazon Redshift, and Databricks. Platform selection is driven by your scale, query patterns, team SQL literacy, existing cloud provider, and cost profile. We do not have default platform preferences or commercial partnerships that influence recommendations. See engagement models for how we scope the platform decision.
A focused data warehouse build for a single business domain typically completes in 6 to 10 weeks. Multi-domain warehouses covering sales, marketing, and operations together run 12 to 20 weeks. We phase work so each phase delivers a working, queryable layer rather than a big-bang launch at the end.
dbt is a transformation framework that lets your team write SQL data models with version control, testing, documentation, and lineage. Most teams with more than one analyst and more than three data sources benefit from dbt. We implement dbt as part of the transformation layer so your data team inherits a maintainable codebase rather than raw SQL scripts scattered across notebooks.
Data engineering engagements typically run between $40,000 and $250,000 depending on the number of data sources, warehouse complexity, streaming requirements, and whether data quality governance is included. We scope before we quote. See data engineering pricing for the full breakdown.
We turn away work that is not the right fit. This is what good fit looks like.
Not sure? Tell us about your data situation and we will tell you whether data engineering is the right starting point.
Tell us about your data sources.
We review every brief and respond within two business days. No commitment. No pitch.
Submit brief → call within 48 hours → architecture proposal in 3 days → pipeline build starts week 2
We will review your data situation and send an architecture proposal within 3 business days.
