Enterprise
Data Engineering
The best data platform is the one your team actually trusts.
Most data problems aren't technology problems. They're trust problems. Teams argue over which number is right. Reports go stale. Pipelines break and nobody finds out until a dashboard reads zero.
We fix the foundation so people can rely on the data they're looking at. Then we make the rest faster.
Symptoms of a trust problem
Most data work starts when nobody trusts the dashboards anymore. Or worse, when two people are staring at the same one and seeing different numbers.
Two dashboards disagree, nobody knows which is right
Same metric, two numbers. People pick the one that supports their argument. Decisions get made on the worse one half the time.
Pipelines break and you find out from a customer
Or worse, from an exec asking why the number on their dashboard is 0. There's no alert because nobody set one up.
There's no agreed definition of an active user
Or any other core metric. Marketing has one. Product has another. Finance has a third. None of them match.
Reports are 'mostly right' with verbal caveats
The analyst presents the number. Then they explain which slice doesn't really count. Then they note the date the source changed.
The same query has three different SQL files behind it
One in the warehouse, one in the BI tool, one in someone's laptop. They've drifted. Nobody's reconciled them in a year.
Backfills are a major operation
Nobody volunteers to run them. The last one took a week and broke two downstream dashboards. So they don't happen.
Capabilities
Ingestion pipelines
Batch and streaming with retries, backfills, and clear SLAs. When a source goes down, you know within minutes, not days.
Lakehouse and warehousing
Snowflake, BigQuery, Databricks, Postgres. We pick the one that fits your data, your team, and your budget - not the one with the splashiest demo.
Modeling and transformation
Dimensional models, Data Vault, dbt projects with real tests. The transformations are readable and the lineage is honest.
Quality and governance
Freshness, schema, and validity checks. Data contracts. Access controls and an audit trail that exists by default.
Catalog and lineage
Ownership, discoverability, and lineage. Engineers can find what they need without asking three Slack channels.
Semantic layer and BI
One definition of an active user. One definition of revenue. Dashboards that answer operational questions instead of starting arguments.
How we approach it
Start with one decision the data needs to support. Prove that decision can be made on solid numbers, then expand. If quality, ownership, or governance can't be operated confidently, that's what we fix first.
-
Assess
Align on the decisions data needs to support. Inventory sources, clarify ownership, surface what 'trustworthy' actually means to the teams using it.
Don't move on until 'trustworthy' has a definition everyone agrees on.
-
Model
Define a minimal semantic foundation. Core entities, metric definitions, and contracts. Quality checks and access controls are part of the design, not bolted on later.
Don't move on until the team can read the model without asking us.
-
Build
Implement ingestion and transformations with backfills, retries, and observability. Pipelines are deterministic and testable, not a sequence of brittle scripts.
Don't move on until pipelines fail loudly and recover cleanly.
-
Enable
Deliver documentation, lineage, and examples so teams can extend the model without breaking downstream dashboards. We hand off, not hover.
We're done when extending the model doesn't need us in the room.
What you'll have at the end
Source inventory and system-of-record map
Prioritized sources, ownership, SLAs, and data-flow diagrams. Dependencies stop being a tribal secret.
Ingestion pipelines with backfills
Batch and stream pipelines that handle retries, backfills, and observable failure modes. When something breaks, you find out from the system.
dbt project foundation
Modeling conventions, CI, tests, and a structure the team can extend without it turning into a maze.
Quality checks and contracts
Schema, freshness, and validity tests. Data contracts that catch breaking changes at the source instead of three layers downstream.
Semantic layer and example dashboards
Metric definitions everyone agrees on, plus dashboards that reflect how the business actually operates.
Documentation and enablement
Lineage, ownership, runbooks, and onboarding notes. The team can operate the platform without picking up the phone.
Outcomes you can point to
Decisions people actually trust
Validated metrics with one definition each. Meetings stop with arguments about whose number is right.
Faster time to an answer
Modeled data and standardized dashboards. The hour of ad-hoc SQL becomes five minutes of clicking around.
Pipelines you can actually rely on
Monitored ingestion and transformations with SLAs. When something breaks, you find out from the system, not from a customer.
Clear ownership
Contracts, lineage, and access patterns that name names. No more 'whose dataset is this'.
Get started
Start with a free consult
Tell us about a report your team argues over, a pipeline that keeps breaking, or a decision you can't make because you don't trust the numbers. We'll work backwards from there.