Data engineering that makes your data reliable
Gjethe builds data pipelines and warehouses: extracting data from your apps, databases, SaaS tools and files, loading it into BigQuery, Snowflake, PostgreSQL or a lakehouse, modelling it with dbt into clean tables, orchestrating runs, and adding data quality checks and documentation. The result is one trusted source for dashboards, analysis and AI projects.
- Free first consultation
- Written quote for your project
- Flexible payment milestones
At a glance
- Warehouses
- BigQuery, Snowflake, PostgreSQL, Databricks
- Pipelines
- Airbyte, Fivetran, custom Python
- Modelling
- dbt
- Orchestration
- Dagster, Airflow, Prefect
Definition
What is data engineering?
Data engineering is building and maintaining the systems that collect, move, clean and store data, so it can be used reliably for reporting, analysis and applications.
Most companies have data in many places: the product database, the CRM, the payment provider, ad platforms, spreadsheets. Reports built directly on each source disagree, break when a column changes and take hours to update by hand. Data engineering brings the sources together into one well-modelled place, refreshed automatically and checked for problems before anyone makes a decision on it.
Who is it for?
Teams with conflicting reports
Sales, finance and marketing numbers that never match.
SaaS and e-commerce companies
Product, billing and marketing data combined for real insight.
Companies planning AI projects
Clean, accessible data before any model can be useful.
Spreadsheet-heavy operations
Manual exports replaced by automated pipelines.
What's included, and what you keep
What's included
- Data source inventory and architecture
- Warehouse or lakehouse set-up
- Ingestion from databases, APIs and SaaS tools
- Custom connectors in Python
- dbt models: staging, intermediate and marts
- Orchestration and scheduling
- Data quality tests and alerts
- Incremental loading and backfills
- Access control and data privacy handling
- Documentation and lineage
- Hand-off to BI tools
What you receive
Handed over at the end. Yours to keep.
- A working warehouse with modelled data
- Pipeline code and dbt project in your repository
- Infrastructure as code
- Data quality checks and alerting
- Data dictionary and lineage documentation
A typical modern data stack
| Layer | Purpose | Common tools |
|---|---|---|
| Ingestion | Copy data from sources | Airbyte, Fivetran, custom Python |
| Storage | Central warehouse | BigQuery, Snowflake, PostgreSQL |
| Transformation | Clean and model data | dbt |
| Orchestration | Schedule and monitor runs | Dagster, Airflow, Prefect |
| Consumption | Dashboards and analysis | Looker Studio, Metabase, Power BI |
Start small, with one question
Data platforms fail when they try to model everything at once. We start with one important question, such as revenue by channel or customer retention, bring in only the sources it needs, and deliver a trusted answer. Each new question then reuses and extends the same foundation.
Data quality is part of the pipeline
A dashboard that shows wrong numbers is worse than none. We add tests that check for missing values, duplicates, unexpected changes in volume and broken relationships, and alert the right person before the data reaches a report. When a source changes its format, the pipeline fails loudly instead of quietly producing nonsense.
Choosing a warehouse
For small and medium data volumes, PostgreSQL can be a perfectly good warehouse and keeps costs low. BigQuery and Snowflake scale further with little maintenance and suit teams with growing data or many analysts. We compare them against your data volume, query patterns, budget and existing cloud before recommending one.
Personal data and privacy
Warehouses often collect personal data from many systems. We design with data minimisation in mind: loading only what is needed, separating or pseudonymising personal fields where possible, controlling access by role and supporting deletion requests. Your legal advisers confirm the obligations that apply to your data.
Signs you need data engineering
- Reports are built by exporting and pasting spreadsheets
- Different teams report different numbers
- Dashboards break when a tool changes
- Analysts spend more time cleaning than analysing
- Nobody knows where a number comes from
- AI projects stall on messy data
One definition of each number
Arguments about numbers usually come from different definitions: revenue with or without refunds, active users counted daily or monthly, customers including or excluding trials. We agree definitions with the people who use them, implement them once in the modelled tables and document them, so every dashboard shows the same answer.
Data for AI projects
AI features and models depend on clean, well-described data. A warehouse with tested, documented tables makes it far easier to build a forecasting model, feed a retrieval system or let an assistant answer questions about your business. Many AI projects that seem to fail on the model actually fail on the data.
How data engineering works, step by step
Every step has a clear output you can review. Timing is agreed in your quote.
- 1
Discover
Sources, questions, users and constraints mapped.
- 2
Design
Warehouse, pipelines and models planned.
- 3
First slice
One important question answered end to end.
- 4
Expand
More sources and models added on the same foundation.
- 5
Harden
Tests, alerts, documentation and access control.
- 6
Hand over
Training and runbooks for your team.
Tools, and what shapes your quote
Pricing is based on your project and agreed in your quote. These are the things that move it.
| Factor | Why it matters |
|---|---|
| Number of sources | Each source needs a connector, mapping and tests. |
| Data volume | Large or fast-changing data needs incremental loading. |
| Data quality | Messy sources need more cleaning logic. |
| Custom connectors | Sources without ready-made connectors need custom code. |
| Modelling depth | More business areas mean more models. |
| Ongoing support | Pipelines need monitoring and updates as sources change. |
| Area | Tools |
|---|---|
| Warehouses | BigQuery, Snowflake, PostgreSQL, Databricks, DuckDB |
| Ingestion | Airbyte, Fivetran, dlt, Python |
| Transformation and orchestration | dbt, Dagster, Airflow, Prefect |
| Quality and docs | dbt tests, Great Expectations, dbt docs |
Common mistakes to avoid
What we see go wrong most often, and how a good project avoids it.
Modelling everything at once
Start with one question and grow from there.
No data tests
Silent errors reach dashboards and decisions.
Logic hidden in dashboards
Business definitions belong in modelled tables, not individual charts.
No owner
Every pipeline needs someone who gets the alert when it fails.
Frequently asked questions
Do we need a data warehouse?
If you combine data from several systems for reporting, usually yes. For small volumes, PostgreSQL can be enough.
Which warehouse do you recommend?
It depends on volume, budget and your cloud. PostgreSQL, BigQuery and Snowflake are our most common choices.
Do you use dbt?
Yes. dbt keeps transformations version-controlled, tested and documented.
Can you connect our SaaS tools?
Yes, using ready-made connectors where they exist and custom Python where they don't.
Do you build dashboards too?
Yes, through our analytics and dashboard services, on top of the warehouse.
How do you handle personal data?
We load only what's needed, control access by role and support deletion requests. Your legal advisers confirm the specific obligations.
Can our team maintain it afterwards?
Yes. We document everything and train your team, or provide ongoing support.
Is this the same as data analytics?
Data engineering builds the reliable data foundation; analytics uses it to answer questions. We offer both.
How is data engineering priced?
It is quoted on the number of sources, data volume, modelling scope and support needs.
Can you load data from spreadsheets?
Yes. Shared spreadsheets can be loaded on a schedule, with checks that catch changes in structure.
How fresh will the data be?
It depends on your needs and sources: some data can refresh several times an hour, other sources once a day. We agree targets per source.
Can you work with our existing BI tool?
Yes. We model data so it works well with Looker Studio, Metabase, Power BI, Tableau or the tool you already use.
Can you migrate our existing pipelines?
Yes. We review existing scripts and jobs, document them and move them to a maintainable stack step by step.
Can we see where each number comes from?
Yes. dbt documentation shows lineage from source tables to the final model behind each dashboard.
Do you handle real-time data?
Yes, where it's genuinely needed, using streaming tools or change data capture. Most reporting works well with scheduled loads.
Related services
Data Analytics
Clean data, clear analysis and decisions you can defend.
Database Design
Schemas, performance tuning and migrations for your data.
Dashboard Development
Live dashboards for operations, sales and finance.
Machine Learning
Prediction, classification and recommendation models.
Python Automation
Scripts that replace manual reports and data syncing.
Data Engineering by industry
Build a data foundation you can trust.
Tell us what you need. You get an honest assessment, a clear plan and a written quote. The first consultation is always free.
- What happens next
- 1You tell us what you need
- 2We aim to reply within 24 hours with questions
- 3You get a written quote for your project