dbt & Data Engineering: Build Pipelines Your Analysts Trust
When analysts spend half their time questioning whether the numbers are right, you have a data engineering problem—not an analytics problem. We build the dbt models, ETL pipelines, and orchestration that keep your data fresh, tested, and consistent.
What Is Data Engineering?
Data engineering is the work of moving data from where it's created to where it's useful—reliably, on schedule, with the right transformations applied so analysts and data scientists can trust what they're working with.
Most companies have data spread across Postgres, Stripe, Salesforce, Intercom, and a dozen SaaS tools. Data engineering connects those sources, loads them into a warehouse, and transforms raw records into clean, business-ready tables.
The tool that changed everything is dbt. dbt (data build tool) lets you write SQL transformations as version-controlled code, with built-in testing, documentation, and lineage tracking. It's become the standard for transformation in modern data stacks because it brings software engineering discipline to SQL.
- Analysts get different revenue numbers from different reports
- Pipeline failures are discovered by business users, not alerts
- Nobody knows what a SQL transformation does or who wrote it
- Data is 12+ hours old by the time analysts see it
- Adding a new data source takes weeks and breaks existing reports
- Your data team spends more time fixing pipelines than building analyses
Decisions We Help You Get Right
The tool isn't the hard part — knowing which one fits your situation is. Here's how we think through the calls that matter most.
ELT vs. ETL
ELT loads raw data first, then transforms it inside the warehouse with dbt — faster to iterate, easier to debug, and it keeps the raw data as a safety net. We default to ELT for new builds and keep ETL only where the source system forces it.
Kafka vs. scheduled batch
If your analytics can tolerate data that's 15–60 minutes old, a scheduled Airbyte or Fivetran pipeline plus dbt is simpler, cheaper, and easier to debug than Kafka. Kafka earns its complexity for fraud detection, live dashboards, and event replay — not as a default.
Snowflake vs. BigQuery vs. Redshift
BigQuery suits GCP teams with sporadic large queries, Snowflake suits mixed workloads spanning clouds, and Redshift Serverless has closed the operational gap for smaller AWS-based teams. The right answer depends on your cloud footprint and query patterns, not brand preference.
Rebuild vs. audit-and-fix
Most teams have good data trapped in unreliable pipelines. We add tests and alerting before considering a rewrite — a full rebuild is reserved for architectures with flaws that incremental fixes genuinely can't reach.
What Reliable Data Engineering Delivers
Data Analysts Can Trust the Numbers
dbt models with built-in tests catch data quality issues before they reach dashboards. When a metric changes, your team knows whether it's real or a pipeline artifact.
Pipelines That Run Without Babysitting
Silent failures are worse than loud ones. We build alerting and retry logic so broken pipelines page your team—not show bad data to your CEO.
One Definition Per Metric
When Sales, Finance, and Product calculate revenue differently, meetings become arguments. dbt's semantic layer enforces one definition, used everywhere.
Tools We Work With
We're tool-agnostic. We work within your existing stack and recommend additions based on your use case, not our preferences.
Frequently Asked Questions
Start With a Free Pipeline Audit
Share your current stack, biggest pain points, and the metrics your team argues about most. We'll review your architecture and tell you what's causing the trust problem—and what it would take to fix it.
If Analysts Don't Trust the Numbers, You Have a Pipeline Problem
Every hour your team spends reconciling conflicting dashboards is an hour not spent on analysis. We fix the pipelines so your data team can do the work they were hired to do.