Data pipelines that hold up, audited from source to dashboard.
Ingestion, warehousing, and transformation infrastructure built to survive contact with real operational data — versioned, tested, and documented well enough that your team can run it without us. Delivered to startups, SMBs, and enterprises across Nepal.
Six capabilities, one accountable team
Pipeline architecture
Batch and streaming ingestion designed against your actual source systems.
Warehouse modeling
Dimensional models on Snowflake, BigQuery, or Postgres — built for the queries you'll actually run.
Transformation as code
dbt models, reviewed in pull requests, tested against data-quality assertions.
Orchestration
Airflow or Dagster DAGs with retries, alerting, and SLA tracking built in.
Data quality
Schema and freshness checks that fail loudly before bad data reaches a dashboard.
Governance & lineage
Column-level lineage and access controls mapped to your compliance requirements.
Four phases, each with a named deliverable
Assess
Source-system inventory and a data model priced against the ingestion volume.
Build
Pipelines and warehouse models stood up in your cloud account, not ours.
Validate
Historical backfill and data-quality checks run against a shadow environment.
Operate
Pipeline monitoring and incident response under an agreed SLA.
-- dbt test run · orders_factmodel = "orders_fact"rows_out = 1284302tests_pass = 18/18freshness = "warn: none"run_time = 41s
Every model reviewed, every run traceable
- Version-controlled transformations — every model change reviewed like production code.
- Documented lineage from raw source to the metric on the dashboard.
- Handed over, not hoarded — your team can extend the pipeline without us in the room.
Built into every engagement
Data engagements on the record
Regional bank — real-time fraud-signal pipeline
Streaming ingestion from 12 core-banking sources feeding a sub-minute risk model.
Retail group — unified sales warehouse
Nine POS systems consolidated into one dbt-modeled warehouse for the first time.
Healthcare network — compliance-grade reporting
Column-level lineage built to satisfy an external data-governance audit.
Questions we get about data engineering
What warehouse platforms do you work with?
Snowflake, BigQuery, and Postgres are the most common targets — the choice depends on your existing cloud footprint, query patterns, and team's SQL fluency, decided during the assessment phase.
How long does a warehouse build take?
A source-system inventory and priced data model come out of the first two weeks. Full build, including historical backfill and data-quality validation, typically runs 6–8 weeks depending on source count.
Who maintains the pipeline after launch?
Your team can, using the dbt models and documented lineage we hand over — or we continue under an SLA-backed operate phase with pipeline monitoring and incident response.
How do you prevent bad data from reaching dashboards?
Schema and freshness checks run on every model and fail loudly before propagating downstream. Every transformation is version-controlled and reviewed in pull requests like production application code.
Can you integrate multiple source systems?
Yes — engagements have consolidated as many as a dozen source systems into a single warehouse. Each source is inventoried and priced individually during the assessment phase.