Case study
Self-service data pipeline platform: 2 500 jobs without data engineers
How a 4-engineer team served data to 3 000+ users — from analysts to business stakeholders — by automating Spark job creation with template generation.
Context
A large foodtech company with 1 700+ locations across 30 countries had accumulated a huge volume of operational data. Analytics was embedded in the business from day one: every order and every click was captured and was expected to turn into management insight.
But demand for new jobs and dashboards kept growing, and the data engineering team could not scale at the same pace.
Problem
Every new data source required a hand-written Spark job: schema, configs, deployment to Databricks. Analysts depended on data engineers even for trivial tasks, and time-to-data stretched into weeks.
- 6-month backlog in the data engineering team
- Analysts blocked on engineers even for repetitive pipelines
- Data engineers turned into a bottleneck between the business and its own data
- Every new source meant hand-rolled schemas, configs and deployments
Solution
We designed and built a code-generation platform on top of the existing stack — tooling that turns simple analyst configs into deployed, production pipelines.
The user fills in a handful of parameters and triggers a GitHub Action. The platform generates Spark code, deploys the job to Databricks and returns the result. The data team stopped writing jobs by hand and started owning templates and quality — no longer the bottleneck.
- Analyst configYAML/JSON
- GitHub Actionpipeline trigger
- Jinja templatecanonical patterns
- ~600 lines of PySparkauto-generated
- Deploy to Databricksproduction-ready
- Analyst configYAML/JSON
- GitHub Actionpipeline trigger
- Jinja templatecanonical patterns
- ~600 lines of PySparkauto-generated
- Deploy to Databricksproduction-ready
«Imagine how long it would take to hand-write hundreds and hundreds of jobs. Now the user creates their own job — without involving the data engineering team.»
Results
2 500+
jobs shipped via the template generator
76 of 85
contributors are not data engineers
4
engineers covering 3 000+ data users
Faster time-to-data
Analysts get a working pipeline in minutes instead of waiting days in the engineering backlog.
Data team back to leverage work
Data engineers moved to architecture and modelling instead of hand-writing yet another job.
Scales with the business
New data domains are onboarded self-serve — without tickets and without the bottleneck.
Operable by design
All generated code lives in one repo — infra optimisations and compute migrations take hours, not weeks.
Want the same leverage on your stack?
Let's map where code generation can take load off your data engineers.
On the call we review your sources, recurring job patterns and current backlog. You leave with a concrete scope: which patterns to templatise, which pipelines to push to self-service, and how to wire it into your existing infrastructure.
- Review of data sources and typical pipelines
- Map of patterns that can be templatised
- Self-service model: who runs what
- Integration with your CI/CD and data platform
- Delivery timeline and engagement model
On the call we review your sources, recurring job patterns and current backlog. You leave with a concrete scope: which patterns to templatise, which pipelines to push to self-service, and how to wire it into your existing infrastructure.