Data engineering

Pipelines, warehouses and data models that give every team the same trustworthy numbers.

Cleaned and conformed: one version of each customer, product and order.

✓order_id is unique and not null
✓customer_id exists in customers
✓status is one of the accepted values

Figure 1. The medallion architecture: data is refined from bronze to silver to gold, with tests at each step. Example tables.

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What we build

Data engineering solutions

We build the pipelines, warehouses and data models that bring your systems' data together, tested and documented, so every team works from the same numbers.

A single source of truth
Consolidate ERP, CRM, finance and product data into one governed warehouse or lakehouse.
Real-time data
Stream events and database changes (change data capture) for near-real-time dashboards and applications.
Data quality and observability
Automated tests, freshness checks and alerts, so problems are caught before reports go out.
Migration and modernization
Move from legacy on-premises warehouses and scripts to a modern cloud platform.
Data for AI and machine learning
Clean, versioned and governed data ready for models and assistants.

An engineer working on equipment in a server rack

How to get started

Three steps to a plan for data engineering

1Tell us what you needUse the project form or book a call. A few sentences about the goal is enough to start.
2Free technical consultationWe go through your goals, users, existing systems and constraints with you.
3Your planA detailed plan covering the right tech stack, architecture, timeline and budget. Then you decide.

Start a project Ask a question first

How it works

The data platform we build

Your data is organized in layers, each with its own tests. Choose a layer to follow one order through it, and switch between batch and streaming.

Scheduled loads, such as hourly or nightly. Simpler and cheaper to run, and enough for most reporting.

Sources

ERP
CRM
Web and app events
Files and spreadsheets

Scheduled extracts and API loads

Used by

Dashboards
ML features
AI retrieval
Applications

One order in silver

Row in stg_orders

order_id   customer_id  amount_usd  ordered_at
SO-10482   C-0931       1240.00     2026-08-03 14:22 UTC

What happened here

✓Duplicate removed: two copies become one row
✓Text amount converted to a number, currency and date standardized
✓Customer ID trimmed and matched to the CRM

Choose Bronze, Silver or Gold above. In a warehouse these are the staging, intermediate and mart layers.

Lineage for every dataset

Delivered with the platform: choose any dataset to see what it feeds and what it depends on.

Selected

crm.accounts

A change here affects 7

stg_accounts, fct_orders, dim_customers, fct_sessions, Churn model, Marketing report, Revenue dashboard

Depends on 0

Nothing upstream: it's a source.

Choose any box. Before changing a source or model, lineage shows who to tell and what to retest.

Our approach

How the work runs

Assess
Map sources, volumes, freshness needs, existing reports and who uses the data.
Design
Choose warehouse or lakehouse, batch or streaming, and a layered model (raw, cleaned, business-ready).
Build pipelines
Automated ingestion and ELT transformations, orchestrated and version-controlled.
Model and test
Business-ready data models with tests, documentation and lineage.
Operate
Monitoring, alerting, freshness targets, access control and cost management.

What we'll need from you

Having these ready keeps the work moving.

Source access
Credentials, API keys or replicas for each source, approved by the system owners.
Metric owners
People who can agree definitions such as revenue, active customer or on-time delivery.
Cloud account
The cloud subscription and warehouse account the platform will run in, owned by you.
A platform owner
Someone to take over runbooks, monitoring and access requests after handover.

Who's on the project

Our team, working with platform owner and metric owners from yours.

¹, ¹, ¹, ², ²

¹ Mayrian ² Your organization

Data architect: Designs the platform, layers, security and cost controls.

Services

Data engineering services

Data architecture and strategy
A target architecture, platform choice and a plan for moving from what you run today.
Pipeline development
Automated ETL and ELT pipelines, batch or streaming, from your source systems.
Warehouse and lakehouse implementation
A modelled, tested and documented data layer on Snowflake, Databricks, BigQuery or Redshift.
Data quality and platform operations
Tests, monitoring, access control, cost management and support after launch.

Deliverables

What you receive

Appendix A. What you receive

All code, data and documentation are handed over in your accounts and repositories.

In your hands

What the documentation looks like

Every project ends with documents your team can run with. Here is an excerpt of one of them.

Data contract

orders: from ERP to fct_orders

Owner: Finance systems · reviewed quarterly

dataset: orders
owner: finance-systems@
schema:
  order_id:     string, unique, not null
  customer_id:  string, not null, references customers
  amount_usd:   decimal(12,2), >= 0
  ordered_at:   timestamp (UTC)
freshness: new orders within 2 hours
quality:
  - row count reconciles to ERP daily close
  - no duplicate order_id
changes: 30 days' notice for breaking changes

Measuring success

How success is measured

What we report on in data engineering projects. Which measures apply, and their targets, are agreed with you at the start.

Table 2. What we report, and when. Targets are agreed with you at the start.

Measure	Reported
Freshness	Every load
Data quality test results	Every run
Reconciliation	Daily, against the source
Pipeline reliability	Weekly
Cost	Monthly

Measure

Freshness

Time since each dataset last loaded successfully, against the target agreed for it.

Reported

Every load

Tests that stop bad data

Simulate an upstream rename and watch the tests we set up hold bad data back from the reports.

Nightly run 02:00

All checks passing

not_null(customer_id)

Every account needs a customer ID; without one, orders can't be matched to customers.

Choose any check to see what it protects. Then simulate an upstream change to see a failure handled.

How to start

From first call to production

Start where you are. Each step ends with a decision, so you commit to the next one only when it makes sense.

Protocol

How an engagement runs

Each step ends with a decision on whether to continue

Free technical consultation

One or two sessions

Talk through the goal, the data you have and the systems involved.

Outputs

(a) A shortlist of use cases, ranked by value and feasibility
(b) A recommended next step
(c) A plan covering stack, architecture, timeline and budget

Book the consultation

Estimate the value

What it could be worth to you

Enter your own figures. The formula is shown, and the estimate can go with your enquiry.

Estimate

Analyst time spent preparing data

Data engineering · from your own figures

Analyst time freed = Analysts × weekly hours × share automated × hourly cost × 52(1)

Analysts who prepare data by hand6Hours each spends a week on it8 hShare automated by pipelines60%Loaded cost per hour$60

Result

$89,856

a year

Add to my enquiry

Build or buy

When you don't need
a custom build

Part of the free technical consultation: when an existing product covers the need, we recommend it instead of a custom build. These are the options we weigh, alongside the tools you already have.

Related work

Existing products that may be enough

[1]Managed connectors such as Fivetran or AirbyteWhen loading standard SaaS sources such as Salesforce, HubSpot or Shopify; these rarely need custom code.
[2]Your BI tool's own connectors, such as Power BI dataflowsWhen reporting draws on one or two systems and a small team.

Our approach

When a custom build is worth it

Sources without a ready connector, including in-house databases and files
Business logic and metric definitions shared across teams
Quality tests, lineage and access control across the platform
Real-time data, or volumes that need cost tuning

In your industry

Where it applies

What this work delivers in the industries we serve.

Technologies and standards

Chosen for your project

Built on the cloud you already use. Choose yours to see the services involved; we recommend the full stack in the free technical consultation.

Table 2. Managed services for each layer, by cloud. The highlighted column is the one you chose.

Layer	AWS	Azure	Google Cloud
Batch ingestion	AWS Glue, Amazon AppFlow	Azure Data Factory	Datastream, BigQuery Data Transfer Service
Streaming	Amazon Kinesis, Amazon MSK	Azure Event Hubs	Pub/Sub, Dataflow
Storage and warehouse	Amazon S3, Amazon Redshift	OneLake, Fabric Data Warehouse	Cloud Storage, BigQuery
Orchestration	Amazon MWAA (Airflow)	Data Factory pipelines	Cloud Composer (Airflow)
Governance and catalog	AWS Lake Formation, Amazon DataZone	Microsoft Purview	Dataplex

Also runs on any of the three: Snowflake, Databricks, dbt, Fivetran, Airbyte, Apache Kafka.

Ingestion

Fivetran
Airbyte
Debezium
Apache Kafka

Transformation and orchestration

dbt
Apache Airflow
Dagster
Apache Spark

Storage

Snowflake
Databricks
Google BigQuery
Amazon Redshift
PostgreSQL

Quality and governance

dbt tests
Great Expectations
Unity Catalog
OpenLineage

Next section

Visualization & analytics

Agreed metrics, drawn so the people who decide can read them.

Data Analytics & AI

2 Visualization & analytics

What happened, and why?

Questions

Common questions
about data engineering

Warehouse or lakehouse?

We recommend one in the free technical consultation. A warehouse suits structured business data and SQL reporting; a lakehouse also handles events, logs and files for analytics and machine learning. Many clients start with a warehouse.

Do we need real-time data?

We build streaming where decisions are made in minutes, such as fraud checks or live operations, and hourly or daily refreshes elsewhere, which are simpler and cheaper to run.

Can we keep our existing BI tool?

Yes, in most cases. We rebuild the data layer underneath the reports people already use in Power BI, Tableau, Looker and other tools.

How is a project priced?

Well-defined scopes are delivered as fixed-price engagements; when requirements are still evolving, we provide a dedicated team instead. Either way, the free technical consultation ends with a plan covering tech stack, architecture, timeline and budget, so you know the cost before work starts.

Data engineering

Data engineering solutions

A single source of truth

Real-time data

Data quality and observability

Migration and modernization

Data for AI and machine learning

Three steps to a plan for data engineering

The data platform we build

Lineage for every dataset

How the work runs

Assess

Design

Build pipelines

Model and test

Operate

What we'll need from you

Who's on the project

Data engineering services

Data architecture and strategy

Pipeline development

Warehouse and lakehouse implementation

Data quality and platform operations