Ingestion pipelines
Batch and streaming ingestion with schema validation at the boundary, not three steps later.
Data Services
Data Engineering & Pipelines
Ingestion, transformation, and quality checks that turn scattered operational data into something a model or a report can rely on.
Overview
Model quality is usually a data problem. Records arrive in inconsistent formats, identifiers do not match between systems, and nobody can say which copy is authoritative.
We build the pipelines and the tests around them: schema validation at ingestion, documented transformations, and alerts when a source starts sending something unexpected.
What you receive
- Ingestion and transformation pipelines under version control
- Data quality test suite with alerting
- Documented schema and lineage
- Warehouse models with example queries
- Backfill and recovery procedures
Typical stack
- Python
- dbt
- Airflow
- PostgreSQL
- S3
- DuckDB
Capabilities
What this covers in practice
Transformation layers
Version-controlled, tested transformations with lineage from source column to final table.
Data quality monitoring
Automated checks on volume, nullability, and distribution, with alerts on breach.
Warehouse modelling
Dimensional models that answer the questions your team actually asks, at query speeds they will tolerate.
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Read moreWant to talk through a data engineering & pipelines project?
Send us the shape of the problem and we'll come back with a scoped approach, a timeline, and an honest read on what's achievable.