DataInn Technologies

Data foundation

Data Engineering & Platforms

Lakehouse and warehouse platforms, streaming and batch pipelines, and the orchestration, testing, and observability that keep them trustworthy.

Data Engineering & Platforms

We build the data foundation that analytics, machine learning, and agents depend on: reliable pipelines, modelled data, and a platform your team can operate.

What we build

  • Lakehouse and warehouse platforms: Databricks, Snowflake, BigQuery, Microsoft Fabric, and open table formats (Iceberg, Delta)
  • Pipelines: batch and streaming ingestion with dbt, Spark, Kafka, Airflow, and Dagster
  • Data modelling: dimensional and Data Vault models designed with the business, documented, and tested
  • Data quality and observability: contracts, tests in CI, freshness and volume monitoring, lineage

How we work

  1. Platform assessment: current pipelines, cost, reliability, and the gaps blocking your use cases
  2. Target architecture: a pragmatic design your team can run, with a migration path
  3. Build in increments: each sprint lands production pipelines with tests and documentation
  4. Hand over or stay: your engineers own the platform; we stay on for operations if you want

Outcomes we measure

  • Pipeline reliability and time to recover
  • Cost per terabyte processed and stored
  • Time from new source to trusted table
  • Adoption by analytics, ML, and agent workloads