Data Engineering
Value over vanity: focus on the smallest data work that enables a real decision.
Good decisions need trustworthy, timely data. We design and build pragmatic data platforms - from ingestion to semantic layers - so teams can move from ad-hoc reports to reliable, repeatable insights.
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
- Assess
- Model
- Build
- Enable
- 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.
- 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.
- Build: Implement ingestion and transformations with backfills, retries, and observability. Pipelines are deterministic and testable, not brittle sequences of scripts.
- Enable: Deliver clear documentation, lineage, and examples so teams can extend safely without breaking downstream dashboards or metrics.
We start small and prove value quickly. Each phase is a gate. If data quality, ownership, or governance cannot be operated confidently, we fix the foundations before expanding scope.
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.
Start with a free consult
Review a concrete decision or reporting need, identify the minimal data foundation required, and decide whether a focused data pilot is worth building.