Data Engineering Services

Ortem Technologies builds the data infrastructure that sits underneath analytics dashboards, ML models, and product features — the pipelines, warehouses, and quality checks that most teams only notice when they break. Data engineering is invisible when it's done right and expensive in engineering time when it isn't: analysts hand-cleaning exports, dashboards nobody trusts because the numbers drift, ML teams blocked waiting on a feature that should already exist.

Warehouse, lake, or lakehouse?

The right platform depends on what you're optimising for. A data warehouse (Snowflake, BigQuery, Redshift) is the right default when your primary need is fast SQL queries for BI — dimensional modelling and star schemas make dashboards fast and consistent. A data lake makes sense when you need to retain raw, unstructured data cheaply — event streams, logs, ML training sets — without committing to a schema up front. A lakehouse (Databricks, or Snowflake/BigQuery with an open table format like Iceberg) is increasingly the pragmatic middle ground: cheap storage with warehouse-grade query performance, so you're not maintaining two platforms and syncing them.

Why pipelines fail in production

Most broken data pipelines aren't broken by a bug — they're broken by an assumption that stopped being true. A source API changed its schema, a currency field started arriving in cents instead of dollars, a duplicate webhook fired twice. We build pipelines with data quality checks (Great Expectations, dbt tests) and freshness alerting as first-class citizens, not an afterthought bolted on after the first incident — so a broken assumption surfaces as an alert, not a wrong number three dashboards downstream.

Ready to fix your data foundation? Book a free data architecture review → Tell us your current stack and what's breaking down; we'll tell you what's actually worth rebuilding first.