Turn Scattered Data Into Decisions
Warehouses, pipelines, dashboards — single source of truth, not 5 tabs.
DIRECT LINE — +92 314 7046916
Every team has 12 SaaS tools and zero unified view. Revenue is in Stripe, leads are in HubSpot, product usage is in PostHog, support tickets are in Intercom. Decisions get made on whichever number someone happened to look at this morning. A real data layer makes this go away.
We build the boring, durable layer first: a managed warehouse (Snowflake, BigQuery, or Postgres if scale is small), Fivetran/Airbyte for ingestion, dbt for transformations, and a BI tool the team will actually use. Real-time and ML come later, on top of that foundation.
- 01Data warehouse setup (Snowflake / BigQuery / PostgreSQL)
- 02ETL/ELT pipelines (Fivetran, Airbyte, custom)
- 03dbt models with tests, docs, and lineage
- 04BI dashboards (Metabase, Looker, Hex)
- 05Reverse ETL (warehouse → CRM / ops tools)
- 06Event tracking schemas (PostHog, Segment)
- 07Real-time analytics where it actually matters
- Snowflake, BigQuery, PostgreSQL
- Fivetran, Airbyte
- dbt
- Metabase, Looker, Hex
- PostHog, Segment
- Apache Kafka (when streaming is real)
REAL PROJECTS, ANONYMIZED ON REQUEST.
Most of our work is under NDA. Reach out for a walkthrough of relevant projects in data engineering & analytics — we will share scope, architecture, and outcomes for engagements that match yours.
[ REQUEST A WALKTHROUGH ]QUESTIONS WE GET A LOT.
Snowflake, BigQuery, or PostgreSQL?
PostgreSQL is fine up to ~100GB and a small team. BigQuery is the cheapest serverless option at scale. Snowflake is the most flexible. We pick based on volume, query patterns, and what your team can operate.
Do we need a data team?
For setup, no — we ship a working pipeline + dashboards. For ongoing analytics, you need at least one person who owns the data layer (analyst, analytics engineer, or technical PM). We can train your existing team during the build.
What about ML and real-time?
Both are real but expensive to start. Most teams get 80% of the value from a clean batch warehouse and good dashboards. We add streaming or ML when there is a specific decision that depends on it, not as a default.