Data
Data systems your operators, auditors, and executives can all defend.
The failure mode is not “we need a warehouse.” It is parallel definitions, mystery transformations, and metrics nobody will sign. We engineer data as a product: explicit contracts, testable quality rules, lineage you can trace, and interfaces that downstream automation and BI can rely on—without turning every question into a bespoke SQL hero project.
What this practice covers
From raw events to governed metrics—not science fair science projects
We design and operate pipelines, models, and consumption layers aligned to how your business actually measures outcomes—not how a textbook data mesh diagram imagines it. That means naming owners, defining SLAs for freshness and correctness where they matter, and making exceptions visible instead of hiding them in a spreadsheet only Finance trusts.
Engagements pair naturally with Automation (features and workflows that consume curated data) and Infrastructure (residency, encryption, access boundaries, and cost guardrails). When those layers disagree, every dashboard becomes a negotiation.
Domains
Where we spend engineering time
Ingestion & integration
Batch, streaming, and API-driven sources with backfill, deduplication, and idempotency where the business requires it.
We treat ingestion as production software: schema drift handling, dead-letter paths, replay procedures, and monitoring that surfaces lag and error budgets—not only “job succeeded” banners. Third-party SaaS exports and legacy databases get the same discipline as greenfield events.
Modeling & contracts
Dimensional and entity models, semantic layers, and field-level contracts tied to owners.
Conformed dimensions, slowly changing dimensions, and bridge patterns where they reduce ambiguity—not ceremony. Contracts document meaning, allowed values, and breaking-change policy so consumers know when a migration is real.
Quality, observability & lineage
Tests on assumptions that matter: uniqueness, referential integrity, business rules, and anomaly thresholds.
Data quality is not a single score. We wire checks to severity, routing, and remediation owners; lineage from source through transforms to exports; and observability that ties pipeline health to the KPIs executives actually read.
Consumption & APIs
Curated datasets, governed APIs, and patterns automation and BI can share.
Reverse ETL only where it earns its complexity; semantic layers and metric definitions that prevent ten versions of “revenue”; access patterns compatible with your identity and classification policies.
Delivery mechanics
How work is structured
Warehouse, lakehouse, or hybrid
Tool choice follows constraints: cost, skill base, latency, residency, and existing contracts. We bias toward boring, well-understood patterns your team can operate— documented tradeoffs instead of resume-driven novelty.
Transformation & orchestration
DAGs with clear failure semantics, environment promotion, and secrets handled like production credentials. Tests on transforms and incremental models where they prevent silent drift.
Security & privacy by design
Classification tags, row-level policies where required, retention aligned to legal hold, and access reviews supported by evidence. For how we summarize hosting and subprocessors at the firm level, see Trust & security.
Cost & FinOps hygiene
Query patterns, partition strategies, and workload isolation so analytics spend scales predictably. Chargeback or showback models that finance can reconcile—not surprise invoices after a quarter of ad hoc scans.
Programs
Typical entry points
| Program | When it fits | Typical outcomes |
|---|---|---|
| Trusted metrics layer | Leadership cites different numbers in the same meeting; finance and operations maintain shadow spreadsheets. | Canonical definitions, documented lineage, quality gates on publish, and a single path from raw to board-ready metrics. |
| Pipeline hardening | Jobs “usually work”; failures are triaged by whoever is brave enough to open the logs; backfills are scary. | Observable SLAs, replay procedures, test coverage on critical transforms, and on-call ownership that matches reality. |
| Product & ops analytics | Product and customer-facing teams need event streams and curated aggregates without waiting on a central bottleneck every sprint. | Event taxonomy discipline, curated marts, governed APIs, and patterns that scale past the first dashboard. |
| Regulated & sensitive data | HIPAA, financial, or sector rules require demonstrable controls on access, retention, and processing—not good intentions. | Classification touchpoints, access patterns, audit-friendly lineage, and documentation your risk team can review once. |
Deliverables
Artifacts that survive turnover
- Source-to-consumption lineage maps with owners and SLAs
- Data dictionaries and contracts tied to breaking-change policy
- Quality rule catalog: what is tested, severity, and who gets paged
- Promotion and rollback procedures for schema and model changes
- Access and classification narrative aligned to your identity stack
Honest boundaries
We are not a generic “AI data” slide factory. Where models or LLMs touch your estate, evaluation, versioning, and human gates belong in the same program as storage and access—not as a side experiment.
We also do not promise that a warehouse alone fixes organizational politics. Our job is to make the technical substrate clear enough that disagreements are about policy, not mystery SQL.
Related practices
Data feeds automation and rests on platforms you can recover. For adjacent depth, see Automation, Infrastructure, and IT services. The full program model lives on Solutions.
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