Data.
Data engineering builds the plumbing - warehouse, dbt, contracts, streaming when it earns its keep. Analytics turns that into dashboards a board can read and metrics the whole company agrees on. Different motions; same practice because one is nothing without the other.
Data engineering
Stack we live in
Snowflake · BigQuery · Databricks · dbt · Airflow · Kafka · Flink · Fivetran · Airbyte
- Warehouse design + implementation (Snowflake, BigQuery, Redshift)
- dbt project structure that doesn't collapse under its own weight
- Data contracts between operational + analytics teams
- Streaming pipelines (Kafka, Flink) when they're actually needed
- Reverse-ETL back to operational systems
- Analytics engineering handover to your team
Reports don't agree with each other. Or your analysts spend their week debugging pipelines instead of asking questions.
Analytics
Stack we live in
Metabase · Looker · Tableau · Superset · Cube · dbt Semantic Layer · Rill · Hex · Sigma
- BI stack pick + implementation (Metabase, Looker, Tableau, Superset)
- Semantic layer so metrics mean the same thing everywhere (Cube, dbt Semantic Layer)
- Executive dashboards that survive contact with reality
- Self-serve enablement - templates, docs, office hours for analysts
- Analytics ops - refresh cadence, alerting on stale/broken data
- Metric governance so a KPI change follows a review, not a Slack DM
Every exec dashboard tells a different story. Analysts spend Fridays reconciling reports. Or you're rolling out BI to a wider audience and it can't stay one analyst's Google Sheet.
Warehouse first, or analytics first?
Tell us what your analysts are debugging on Fridays and what your execs disagree about on Mondays. A principal architect replies within one working day.
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