Data engineering services
Pipelines that move your data reliably, every day. We bring data in from your apps, databases, files and SaaS tools, clean and model it, and make sure someone hears about it the moment a job fails.
What’s included
Batch and streaming pipelines
Spark, Kafka or Flink when the volume needs it, and simpler tools when it doesn’t.
ETL and ELT
Orchestrated with Airflow, modelled and tested with dbt.
Sources of every kind
APIs, databases, files, spreadsheets and SaaS tools.
Data quality checks
Row counts, totals, duplicates and freshness checked on every run.
Alerts and monitoring
A failed or late job alerts someone straight away.
When it makes sense
- Reports depend on someone exporting and merging files.
- Your data lives in many tools and none of them agree.
- Jobs fail silently and you find out from a wrong number.
What you get
- Pipelines in your cloud, with the code in your repository
- Data quality checks and alerts
- Documentation of every source and table
- A runbook for when something fails
Tools we work with
We pick what fits your project and your team, and we’ll say why.
- Python
- SQL
- Spark
- Kafka
- Airflow
- dbt
- Databricks
- Snowflake
- BigQuery
Related work
Built by our engineers before Orandic. None of these clients hired Orandic.
Questions about data engineering
Do we need streaming?
Usually not. Most business reporting works well with data refreshed every hour or every morning. Streaming is worth it when decisions need data within seconds.
Can you work with our existing data team?
Yes. We can build alongside them, review their pipelines or take on a piece they don’t have time for.
Related services
Tell us what you need built.
A free 30-minute call with an engineer. You get the scope, timeline and price in writing before you commit, and a reply within one working day.