Business intelligence & analytics
Metrics leaders actually trust. Analytics engineers who define grain first.
When finance and product disagree on a number, the problem is usually definitions, not tooling. We staff analytics engineers who stabilize metrics with dbt exposures and governed LookML, tune Tableau extracts that survive month-end, and publish grain and caveats before charts reach leadership. Duplicate reports shrink because one canonical model feeds every dashboard.
Discuss your analytics roadmapwith orders as ( select order_id, customer_id, date_trunc('month', created_at) as revenue_month, sum(net_amount) as net_revenue from {{ ref('stg_orders') }} where status = 'paid' group by 1, 2, 3)select revenue_month, count(distinct customer_id) as active_customers, sum(net_revenue) as monthly_net_revenuefrom ordersgroup by 1Core stack
- SQL & warehouse tuning
- Looker / LookML
- Tableau
- Power BI
- dbt analytics
- Python analysis
5+
Average years in analytics engineering
Engineers who've owned exec dashboards, not only exported CSVs from admin panels.
Deep-Dive Tech Stack
Trusted analytics needs warehouse logic, semantic layers, and BI tools telling the same story. We match engineers who define grain before visualization, not analysts who ship charts without saying whether the metric is daily or monthly unique.
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SQL & warehouse tuning
Window functions, cohort logic, and queries that do not burn Snowflake credits on full scans. Logic lives in tested dbt models instead of 400-line ad hoc SQL in BI tools nobody can audit.
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Looker / LookML
Governed explores with permission sets and derived tables with documented grain. Explore sprawl that double-counts revenue gets consolidated before every PM clones another broken join.
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Tableau
Extract schedules aligned to close cycles and performance tuning on high-cardinality dashboards. Hyper extracts, efficient filters, and documented sources let analysts trace numbers past the demo.
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Power BI
DAX measures with explicit filter context and incremental refresh for large datasets. Slicers that change grain silently are caught in review before finance stakeholders trust a broken total.
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dbt analytics
Staging and marts with schema tests, documentation YAML, and CI promotion. Duplicate reports often drop 50%+ when teams converge on dbt exposures instead of siloed SQL in each tool.
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Python analysis
Pandas for ad hoc work with pinned notebooks; recurring exec asks graduate to Airflow or dbt instead of permanent notebook debt on someone's laptop.
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Metric catalogs
Definitions, owners, lineage, and refresh SLAs in Atlan or markdown registries. Active users maps to one dbt model with published grain, not three conflicting threads before every board meeting.
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Metabase & self-serve BI
Curated collections, row-level security aligned to warehouse roles, and governed filters so business users explore without writing SQL that bypasses grain. Ad hoc questions stay fast without duplicating metrics that already exist in Looker or dbt.
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Reverse ETL (Census / Hightouch)
Syncing warehouse segments to CRM, marketing tools, and support platforms with idempotent jobs and freshness SLAs. Activation and sales teams get audience lists from the same metric definitions finance uses, not from CSV exports that stale overnight.
Analytics outcomes we optimize for
- Average years in analytics engineering
- 5+
- Typical time to first trusted dashboard
- 2–4 wks
- Reduction in duplicate reports
- 50%+
- Documented metric definitions
- 100%
Engineers who've owned exec dashboards, not only exported CSVs from admin panels.
For scoped metrics with warehouse access, staging data, and signed-off definitions.
After consolidating metrics and semantic layers on programs we've supported.
Grain, filters, and caveats published before a chart reaches leadership Slack.
Analytics staffing, answered plainly
How do you handle time-zone crossovers?
We align overlap for requirements workshops and steering reviews. Async Loom walkthroughs and documented metric specs cover ongoing work across US, EU, and India teams.
Do your engineers work in our warehouse and BI tools?
Yes. We operate in your Snowflake, Looker, Tableau, or Power BI under your access policies. We don't require migration to our tooling.
How do you prevent metric drift between teams?
Shared semantic definitions, dbt or LookML sources, and a single owner per core metric. Ad hoc SQL gets labeled as exploratory, not canonical.
Can you support finance and product stakeholders?
Yes. We translate questions into testable definitions, build refreshes that match close cycles, and document caveats before numbers reach the board deck.
Who owns the dashboards and SQL?
You do. All models, queries, and workbooks live in your systems under your terms.
