Build the data systems your work needs.
Our capabilities connect across the platform, from source integration and shared models to analysis and continued improvement. Start with the work that needs to change.
Different work.
Shared foundations.
Operational questions, scientific measurements, and AI evaluations need data that can be traced, reviewed, and used.
Model the work
behind the number.
We connect operational events, resolve their sequence, and define the grain behind each measure. Completed work, open items, and invalid records stay distinct, so teams can trace a reported number to the work it describes.
Five completed items. A 15-minute mean. The open item stays outside the denominator.
Models & governance · Analytics & decision supportDuration per work item · minutes
Keep every result
connected to its method.
We connect instrument exports, sample identifiers, method versions, and quality flags. Models preserve replicate grain and distinguish accepted results from readings held for review, so an analysis can be traced to its inputs.
Show the accepted measurements and the exception. Keep method and calibration references attached.
Pipelines & integration · Models & governance · AnalyticsMeasurements · µg/mL
Accepted: 8.1, 8.3, 8.2
Accepted: 12.0, 12.4 · Held: 18.7
Build evaluation data
you can compare.
We structure evaluation runs around versioned cases, configurations, review criteria, and expert judgments. The same slice definitions carry through latency and usage estimates, while unresolved assessments remain visible.
Execution data, expert assessment, and estimated cost have different meanings. Keep all three in view.
Models & governance · Analytics & decision supportEach segment represents one evaluation case.
From source records
to a shared answer.
A measure moves through the source, the model, and the view. Its definition should travel with it.
Worksheetsselect work_item_id, event, event_at
from raw.events order by event_at;| Work item | Event | Time |
|---|---|---|
| W-101 | Started | 09:00 |
| W-102 | Started | 09:05 |
| W-101 | Updated | 09:06 |
| W-101 | Completed | 09:12 |
| W-102 | Completed | 09:23 |
Five events describe two work items.
select work_item_id,
datediff('minute',
started_at, completed_at
) as duration_minutes
from {{ ref('int_work_lifecycle') }}
where completed_at is not null
and terminal_state = 'completed'
and completed_at >= started_atOne row per completed item. Open and canceled items stay outside this measure.
Power BICompletion time
work_item_cycle→reportThe report uses the model’s grain, exclusions, and UTC completion day.
The stack
follows the work.
Cloud infrastructure, source systems, processing, and analytical tools have different jobs. We design the connections around the data, the decisions it needs to support, and the people maintaining the result.
Make the architecture
fit its environment.
We connect storage, processing, access boundaries, and environments. Infrastructure changes should be reviewable, with the configuration and operating responsibilities clear.
Cloud & infrastructure
AWS · Microsoft Azure · Google Cloud · Terraform
Infrastructure configuration, resource lifecycle, access, networking, and environment boundaries. Terraform manages resources; orchestration manages dependent work.
Platforms & databases
Snowflake · BigQuery · Databricks · Amazon Redshift · Microsoft Fabric · PostgreSQL · Microsoft SQL Server
Choose storage and processing patterns around source constraints, workload, concurrency, and measured behavior. These platforms have different roles and can be alternatives.
Plan → review → apply
AWS
Amazon S3
Looker
Azure
Azure StorageDatabricksPower BIMove data.
Coordinate the work.
Source behavior determines the loading pattern. Dependencies, readiness checks, retries, and recovery determine how the work runs.
Orchestration
Apache Airflow · Dagster · Prefect
Schedules, dependencies, input readiness, backfills, alerts, and owners. A late source and a failed transformation need different responses.
Ingestion & event movement
Fivetran · Airbyte · Apache Kafka · custom Python/SQL API, file & database integration
Managed connectors, custom extraction, CDC, and event streams have different uses. Define source identifiers, updates, deletions, schema changes, reconciliation, and recovery.
Models & processing
dbt · SQL · Python · Apache Spark
Model layers, reusable logic, tests, documentation, incremental builds, lineage, and releases. Jinja supports reusable SQL within dbt.
Apache Airflowdbtheld · owner assignedcheckpoint · safe retryA useful view starts
with a defined question.
We build shared measures, dashboards, and analytical investigations. Assumptions and interpretation belong alongside the visual.
BI & analytical workspaces
Microsoft Power BI · Looker · Tableau · Hex · Metabase · Apache Superset
Shared definitions, analytical views, exploration, interpretation, and recommendations. A dashboard is one output; investigation explains what it can support.
Research & exploratory work
Jupyter · Hex · Python · SQL
Versioned inputs, source references, units, experiment identifiers, and reviewed analytical outputs. Research teams retain ownership of scientific interpretation.
BigQueryGrain · units · exclusions · ownerPower BILookerTableau / HexConnect systems.
Preserve their meaning.
Operational systems, financial records, and research sources carry different identifiers and rules. We preserve those distinctions through integration and modeling.
CRMs & operational systems
Salesforce · HubSpot · Microsoft Dynamics 365 Sales · Pipedrive · Zoho CRM
Records, activities, and status changes with stable identifiers, source timestamps, ownership, and appropriate access.
Activation, billing & payments
Hightouch · Stripe · Adyen · Orb
Governed destination updates and scoped financial analysis. Keep billing, cash collection, and recognized revenue distinct; Finance owns accounting policy.
Laboratory & research data
LIMS · ELN · instrument files · experiment metadata · evaluation records · object storage
Sample and run identifiers, units, method versions, quality flags, source references, and access boundaries. These are system and data types, not claims of every vendor integration.
source ID · updated_at · statesample · replicate · unit · methodcase · config · assessment · runWhat does your data need to do next?
Tell us what is changing, where the current system gets in the way, and what your team needs to decide.