We build data pipelines that collect data from Leon, Jira and internal systems automatically, prepare it and write it into a model you can report on — with logging, error handling and measurable data quality rules to accept against.
Companies work with complex, often fragmented data sources: Jira, ERP, finance, Excel lists, manual exports — and Leon on top of that in flight operations.
This data is rarely consistent, clean or structured – and prevents precise reporting, stable decision paths and operational automation.
What comes out is a basis for dashboards, metrics and automation — loaded in time windows, guarded against concurrent runs and logged run by run.
ETL stands for Extract, Transform, Load – a core technical process that turns raw data from Leon, Jira and other systems into clean, consistent and analysable information.
We extract data efficiently and incrementally from Leon, Jira or internal systems:
Extraction is performed via optimized GraphQL queries or APIs – efficient, stable, and without full imports.
During the transform phase, raw data is refined into a consistent, reliable, and production-ready foundation:
The outcome: clean, structured data with clear relationships and enterprise-grade data quality.
The final data is loaded into a high-performance SQL database or a central data warehouse:
From data extraction to automated monitoring – every stage is designed to make your data clean, consistent, and reliably usable.
We extract data from Leon, Jira and other systems in a structured and high-performance manner:
All data records are uniquely identified and prepared for further processing.
The data is automatically cleansed, validated, and transferred into a harmonized data model.
Highlights:
Structured data is written into a scalable SQL database – perfect for BI, dashboards, and automations.
Features:
We ensure that your data pipelines run reliably and continuously.
Services:
Result: clean, available, and consistent data – at all times.
Where prepared data turns into recurring cases, Jira Automation can take the next step once the interface has been agreed.
We begin by defining which sources are connected, which data objects are needed from them and which target system they go into. This requires access to the sources or representative extracts, plus someone who knows what the fields mean in practice.
Initial load and ongoing updates are planned separately: the initial load is a one-off run across the existing data, updates then run at the agreed interval. Acceptance includes measurable data quality rules — which fields are mandatory, which values are valid, how exceptions are handled.
You receive the pipelines with logging and error handling, the data model in the target system and a description of the processing steps.
Not included: running the pipelines after acceptance, following up on changes to the source systems, and the licences of the source and target systems. If a vendor changes its interface, that is a new engagement — not a warranty case.
Tell us briefly what it is about – we will get back to you.