97%
of senior data leaders say pipeline failures have slowed analytics or AI initiatives.
Fivetran, 2026
AI & Data
Data you can actually trust.
Connect data from databases, APIs, spreadsheets and SaaS into one source of truth. Build the pipelines, platforms and analytics your team relies on, with the foundation that lets you add AI when you are ready.
Data your business can actually act on.
If your data is scattered across systems, pipelines break on Friday afternoons, and nobody can answer "what does this column mean?", then your dashboards lie, reports go stale and your team stops trusting the numbers. We rebuild the foundation. Pipelines that survive messy real-world sources. Schemas that grow with your business. Governance that lets analytics and AI run safely on production data. Your team stops fighting the data and starts shipping insights.
By the numbers
Bad data is expensive. Good data engineering pays for itself.
Most data problems are not about analytics or AI models. They are about the plumbing underneath.
$12.9M
average annual cost of poor data quality per organisation, according to Gartner.
Gartner, 2024
53%
of engineering time is spent maintaining and fixing pipelines instead of building new features.
Fivetran, 2026
80%
of enterprise data initiatives fail or underperform due to poor data engineering, not poor analytics.
Industry analysis, 2026
What you get
Six capabilities that make up most data engineering engagements.
Pipelines that do not break on Friday afternoon
Idempotent, observable batch and streaming pipelines on Kafka, Spark, Dataflow or your existing stack. Built to recover from failure, not fall over on it.
A data platform sized for your data
Right-sized warehouses and lakehouses on Snowflake, Databricks, BigQuery or open-source equivalents. We pick the cheapest stack that meets your scale, not the trendiest one.
Dashboards your stakeholders actually trust
Analytics and reporting layers on Power BI, Tableau, Looker or embedded analytics. With semantic models so the numbers match across teams.
Upstream changes stop breaking what is downstream
Data contracts and schemas that absorb changes upstream without breaking the dashboards, reports and models depending on them.
Governance your auditors believe in
Catalog, lineage, freshness, quality monitoring, access controls and data protection checks. So your data is safe to use and safe to audit.
A foundation that is ready when AI is
Vector stores, retrieval indexes and clean training data alongside your operational stack. So when an AI use case shows up, the data is already in shape.
Why Codino
- Engineers who have fixed broken pipelines at 3am. We know the difference between a demo that works and a system that survives real traffic.
- No vendor religion. Snowflake, Databricks, BigQuery, open-source. We pick what fits your data and your budget, not what fits our partnership agreement.
- Honest about data quality. We tell you which sources are clean, which are messy, and which ones will cost more to fix than they are worth.
- EU-based, GDPR by default. Data residency, access controls and lineage built in from day one. No retrofitting required.
- Your team owns the platform when we leave. Documentation, runbooks, monitoring and evaluation built in. Not an afterthought.
- Fast to first value. First pipeline shipping data in weeks, not months. You see whether the approach works before scaling.
How we deliver
A pragmatic path from audit to running platform. No multi-year migrations.
- 01
Audit
Map your current data estate, identify the highest-friction sources and design a target architecture. You walk away with a phased roadmap your team can execute.
- 02
Build
Stand up the platform and ship the first use case in full: a critical dashboard, a broken pipeline replaced, a new data product. By handoff, something is already shipping value.
- 03
Govern
Add lineage, quality monitoring and access controls so the platform is safe to scale. Your team starts trusting the numbers; your auditors stop asking awkward questions.
- 04
Operate
Cost optimisation, query tuning and continuous improvement as usage grows. Cost per query stays predictable even as adoption climbs.
What changes when the data layer works
Book a data auditYour business decisions are backed by data, not guesswork
Dashboards your stakeholders trust. Reports that match across teams. Forecasts grounded in clean numbers. And when the next AI project starts, it becomes weeks of work instead of a quarter.
Senior engineers stop firefighting
Pipelines stop being a daily meeting topic. Your engineers spend their time shipping product, not chasing schema changes or midnight alerts.
Your cloud bill stops being a CFO question
Right-sized infrastructure and cost practices keep cloud spend aligned to actual usage. Predictable enough that finance stops asking about it.
Data becomes a real asset, not folklore
Documented, catalogued, owned. Not tribal knowledge in a few engineers' heads. Your next hire onboards by reading docs, not by shadowing the one person who remembers.
Where data engineering lands first
Industries with many data sources, strict compliance needs and high cost of bad data.
Financial services
- Real-time transaction processing and reporting
- Risk data consolidation from multiple systems
- Regulatory reporting with full lineage
- Customer 360 from fragmented sources
Selected work
Recent data engineering projects, from BI platforms and risk-management tools to AI-ready data foundations.
Recast
Recast is a platform that resets what it means to stream. Making access fairer, rewarding creators better, and connecting through content. Where Fans pay creators per view, rather than the platform per subscription.
View Case Study
Centogene
Development of a specialized platform for managing and analyzing mass spectrometry data, facilitating biomarker discovery in rare disease research.
View Case StudyWhat Clients say about us
Pete Willcox
VP Product
"All members of the Codino team fit seamlessly into our delivery teams, building excellent relationships and always willing to go the extra mile to deliver on our Roadmap in a timely and efficient way. We have built extremely good relationships with them and they feel just like part of the team"
Recast
Data engineering, explained
With the smallest foundation that unblocks your most painful problem. Usually one critical dashboard, a broken pipeline or a new data product. From there we expand as more use cases come online, including AI when you are ready for it.
Both. We design pipelines and the analytics layer on top: dashboards, embedded analytics, semantic models, KPI definitions. Your stakeholders see the result, not just the infrastructure that makes it possible.
Snowflake, Databricks, BigQuery, Redshift on the warehouse side. Kafka, Kinesis, Pub/Sub, Dataflow, Airflow, dbt for pipelines and orchestration. Power BI, Tableau, Looker, Metabase for analytics. Cloud: AWS, GCP, Azure. We pick what fits and never push tools you do not need.
Yes, that is how most engagements run. We bring senior engineers and analytics specialists alongside your in-house team, share the production discipline and step out cleanly once your people are autonomous.
Lineage, access controls, data protection checks and EU-region deployments by default. As an EU-based partner, we work to your existing governance framework or help establish one that meets GDPR and your sector-specific regulations. Your privacy review passes the first time, not the third.
Let's Talk About Your Project
Get In Touch
Maciej Roman|CEO & Co-founder