Data Engineering Services

What data engineering involves

Data engineering is the work of collecting data from many sources, cleaning and transforming it, and storing it in a form that analytics, reporting and AI systems can use. It covers pipelines that extract data from applications and APIs, warehouses or lakehouses that hold it, transformations that turn raw records into trustworthy tables, and the orchestration and monitoring that keep everything running.

Good data engineering is invisible when it works: numbers are fresh, consistent and documented. When it is missing, analysts spend their days fixing exports and AI projects stall because the data is not ready.

Problems we solve

• Data scattered across CRM, ERP, product database, spreadsheets and SaaS tools
• Reports that break whenever a source system changes
• Slow queries running directly against production databases
• No history of changes, so trends cannot be analysed
• AI initiatives blocked by data that is incomplete or inaccessible

What’s included

Modern data stack, sensibly sized

There is no need to buy every tool in the modern data stack on day one. For many companies, a managed ingestion tool or a few Python jobs, a cloud warehouse and dbt for transformations cover most needs at a modest monthly cost. We add streaming, a lakehouse or a data catalogue only when volume, latency or the number of data users makes them worthwhile.

Data for AI

AI systems need data that is accessible, current and permissioned. For retrieval-augmented assistants, that means document pipelines that extract text, keep it up to date and preserve access rights. For machine learning, it means feature tables with history and consistent definitions. We design data platforms with these needs in mind, so the next AI project can start in weeks rather than months.

Timeline and budget

Typical ranges to help you plan. Your written estimate comes after a free discovery call.

Batch or streaming?

Most organisations need batch pipelines for nearly everything and streaming for a small number of genuinely time-sensitive use cases. We start with batch and add streaming where the business value of fresher data justifies the extra engineering.

Data quality as part of the pipeline

Bad data that reaches a dashboard or a machine learning model is far more expensive than data stopped at the door. We build quality checks into every pipeline stage:

• Schema checks that catch renamed or missing columns from source systems
• Freshness checks that alert when a feed stops arriving
• Uniqueness, null and range tests on key fields
• Reconciliation of row counts and totals against source systems
• Data contracts with upstream teams so breaking changes are agreed in advance

Ownership and documentation

Pipelines outlive the people who built them. We document lineage — where each table comes from and what depends on it — and keep transformations in version control with code review, just like application code. A data catalogue and clear ownership mean that when something breaks, the right person is alerted and can see the impact quickly.

ETL or ELT?

Traditional ETL transforms data before loading it into the warehouse. Modern ELT loads raw data first and transforms it inside the warehouse with SQL. ELT is usually simpler, keeps a full history of raw data and lets analysts contribute to transformations, which is why it has become the default for most cloud warehouses. ETL still makes sense when data must be filtered or anonymised before it leaves a source system, or when volumes are large enough that transforming in flight is cheaper.

Orchestration and reliability

Pipelines must run in the right order, retry on failure and alert someone when they cannot recover. We use orchestration tools such as Airflow, Dagster, Prefect or managed cloud schedulers, depending on scale. Every pipeline has clear dependencies, idempotent steps that can be safely re-run, and monitoring for late or missing data — not just failed jobs.

• Idempotent loads so reruns never duplicate data
• Alerts for failures, delays and unexpected row counts
• Backfill procedures for reprocessing history
• Version-controlled pipeline code with CI checks
• Cost monitoring for warehouse and compute usage

A typical data platform project

1. Assess1–2 weeks

   Inventory sources, current reports and pain points; agree priorities.

2. Foundation2–4 weeks

   Set up the warehouse, ingestion, environments, access controls and CI.

3. First domain3–6 weeks

   Model one business area end to end — for example sales or finance — and deliver trusted reports on it.

4. ExpandOngoing

   Add domains, real-time feeds or ML features as needs grow.

Durations are typical for a mid-sized business with several core systems.

Controlling warehouse costs

Cloud warehouses make it easy to run up bills through inefficient queries and unnecessary refreshes. We partition and cluster large tables, refresh models incrementally, schedule heavy jobs sensibly and set budgets and alerts. Regular reviews of the most expensive queries usually find quick savings.

Data engineering underpins analytics and BI and AI knowledge systems.

Governance without bureaucracy

As more people rely on shared data, a light governance layer prevents confusion. We tag sensitive columns, restrict access by role, document each table and its owner, and keep lineage so anyone can see where a number comes from. Good governance is mostly good habits built into the pipeline — not a committee that slows every change.

• Role-based access to schemas and sensitive fields
• A data catalogue with owners and descriptions
• Lineage from source system to dashboard
• Retention rules applied automatically
• Masked or synthetic data in development environments

Signs your data platform needs attention

• Reports disagree depending on who built them
• Analysts spend more time cleaning data than analysing it
• Pipelines break silently and nobody notices for days
• Nobody can say where a number on a dashboard comes from

Each of these is fixable step by step, starting with the business area that hurts most.

Related services

Data engineering feeds analytics and BI, machine learning and RAG projects.

This page is part of AI Development & Data Engineering Services — see the full overview for related services, approach and pricing.