Services
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 arguing over which number is right, reports that go stale, pipelines that break and nobody knows until a dashboard is wrong. We fix the foundations so people can actually rely on the data they're looking at.
What we do
Ingestion pipelines
Batch and streaming ingestion with retries, backfills, and clear SLAs.
Lakehouse & warehousing
Snowflake, BigQuery, Databricks, Postgres - right tool, right cost, right constraints.
Modeling & transformation
Dimensional models, Data Vault, dbt projects, and tested transformations.
Quality & governance
Freshness, schema, and validity checks; data contracts, access controls, and auditability.
Catalog & lineage
Ownership, discoverability, and lineage so teams trust data and move faster.
Semantic layers & BI
Consistent metrics definitions and dashboards that answer real operational questions.
How we work
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Assess
Align on the decisions that matter, define success metrics, and inventory sources. We clarify ownership, data sensitivity, and what "trustworthy" means for your teams.
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Model
Define a minimal semantic foundation: core entities, metric definitions, and data contracts. Quality checks and access patterns are treated as design constraints from day one.
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Build
Implement ingestion and transformations with backfills, retries, and observability. Pipelines are deterministic and testable, not brittle sequences of scripts.
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Enable
Deliver clear documentation, lineage, and examples so teams can extend safely without breaking downstream dashboards or metrics.
We start with one decision or reporting need, prove the data is trustworthy, then expand from there. If quality, ownership, or governance can't be operated confidently, we fix that first.
Deliverables
- Source inventory and system-of-record map: prioritized sources, ownership, SLAs, and data-flow diagrams that make dependencies explicit.
- Ingestion pipelines: batch/stream ingestion with retries, backfills, observability, and clear failure modes.
- dbt project foundation: modeling conventions, CI, tests, and a structure teams can extend without chaos.
- Quality checks and contracts: schema/freshness/validity tests, data contracts, and documented access patterns.
- Semantic layer and example dashboards: metric definitions, example KPIs, and dashboards that reflect how the business actually operates.
- Documentation and enablement: lineage/ownership guidance, runbooks, and onboarding notes so teams can operate independently.
Outcomes
- Trustworthy decisions: consistent, validated metrics teams agree on.
- Faster time to insight: modeled data and standardized dashboards reduce ad-hoc work.
- Reliable pipelines: monitored ingestion and transformations with clear SLAs.
- Clear ownership: contracts, lineage, and access patterns reduce ambiguity.
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.