Senior engineers for the warehouses, pipelines, and models your reporting runs on. Databricks and Snowflake, on AWS or Azure, with technical review from Inpro Analytics.
You interview the engineers, who build in your repositories and hand the platform over to your team.
From a warehouse move to dependable reporting. Explore the work and the first improvement your team can use.
Today: Your platform is holding you back.
Move domains in stages, with parallel runs and agreed checks. Your reports keep running while the platform changes beneath them.
First useful milestone An agreed cutover plan and a first domain migrated live, with reporting kept running.
Today: Your team is working around fragile data.
Connect sources through staging, tested business models, and reporting. Give your team a dbt layer with visible freshness checks and documented lineage.
First useful milestone Core models under test, documented lineage, and a CI quality gate.
Scope and timing are agreed after discovery.
Working systems in your repositories, with guidance your team can use to maintain them.
Production pipelines, tested models, and quality checks your engineers can maintain.
In your repository: Pipelines, Tested models, Quality checks.
In your repositories, owned by your team.
Dependencies, target architecture, and a rollout order agreed with your team.
Architecture + migration planRunbooks, lineage, and maintenance notes. Walked through together, with clear owners.
Operating guide + handoverAnonymized engagements, each with the team we placed, how long it ran and what it changed.
We agree the scope and pace around your platform. Your team stays involved in the decisions that matter.
Map sources, dependencies, and reporting consumers. Agree on the outcome that would make the work worthwhile.
Result A written assessment
Choose the target platform, modeling approach, data contracts, governance, and a practical migration order.
Result A target design and plan
Test pipelines and migrate domains with parallel runs. Review the results together before each cutover.
Result Working, verified data flows
Walk your team through runbooks, conventions, lineage, and operation. Agree how ongoing support will work.
Result An operational handover
You review named profiles and interview the engineers. Rates, availability, and start dates are confirmed for your engagement.
Meet the companyAn indicative platform plan allows two weeks for discovery and one to two weeks for architecture, followed by incremental delivery on a two-week cadence. A broader engagement may reach operational handover in month four or later. We agree the scope and timeline together; a focused pipeline improvement can be much smaller.
Yes. The first step is to understand what already works, what needs attention, and what depends on it. The scope can cover a specific pipeline or modeling layer as well as a broader migration.
Directly, in your tools, repositories, and working routines. Technical review through Inpro Analytics supports delivery, and decisions are discussed with your team.
A short outline of your current stack, the problem you want to solve, and any timing constraints. We can work through the detail together.
Meet your specialists.
Select a person to explore their experience.
Showing Arben, Senior Data Engineer
Data engineering
Senior Data Engineer
9 yearswarehouse and pipelines
Senior Data Engineer
Data engineering
Analytics Engineer
6 yearssemantic layer and BI
Analytics Engineer
You interview and approve every engineer. We review technical experience, working style, and language fit.
Typically 2-4 senior engineers, with an indicative rate of €290-450 / day per engineer. Shortlist target: 5 business days from an agreed brief.
Indicative sequence. We confirm timing, capacity, and terms for your role.
Sample profiles from our network. Availability, rates, and fit confirmed for your brief. Language levels use CEFR; full CVs and interview details accompany your shortlist.
Connect ingestion and tested models to the reporting your business relies on. We build around the stack you already use.
Talk through your stackWe work with your existing stack and choose the tools around the job. These are the ones we use most in this area.
A foundation for storing, processing, and serving your data.
Turn source data into tested models your business can use.
Coordinate workloads and keep your pipelines running reliably.
Move events and changes between systems as they happen.
Check your data and understand where every result comes from.
Provision the environments your platform depends on.
Tell us where your data platform needs to improve.
We’ll work out the right next step together.
