Services—Cloud Engineering

Azure Data Engineering & Cloud Consulting

Moving to the cloud is not a data strategy — it is a precondition for one. We engineer cloud data environments that are performant, cost-efficient, and built for the analytics and AI workloads your business actually runs. With deep roots in Microsoft Azure and cloud-native architecture, we have delivered cloud data transformations at enterprise scale — from greenfield lakehouse builds to legacy on-premise migrations to pipeline reliability remediations on existing cloud environments.

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Azure cloud data engineering
10+
Years Azure expertise
40%
Avg. cloud cost reduction
99.9%
Pipeline uptime target
3
Cloud platforms covered

What's Included

Capabilities

01

Azure Data Platform Engineering

End-to-end Azure data engineering across the full Microsoft stack — Azure Data Factory for orchestration, Azure Data Lake Storage Gen2 for storage, Azure Databricks for distributed compute and ML, Azure Synapse Analytics for SQL-based workloads, and Microsoft Fabric for unified analytics. We design these components to work together as a coherent platform, not as isolated tools bolted together. Every design decision is made against your specific analytics requirements — batch vs streaming, SQL vs Spark, self-service vs governed — rather than defaulting to the latest architectural pattern regardless of fit.

02

Data Lakehouse Architecture & Build

The data lakehouse pattern — medallion architecture (Bronze/Silver/Gold) on open table formats — is the right architecture for most enterprise data platforms being built today. We design and build production lakehouse environments on Delta Lake (Databricks) or Apache Iceberg (Snowflake/AWS), with Unity Catalog for governance, dbt for transformation logic, and Azure Data Factory or Airflow for orchestration. The Bronze layer captures raw source data with full history. Silver applies data quality, typing, and deduplication. Gold implements business logic as governed, queryable data products. The result is a platform that serves BI reporting, ML engineering, and AI workloads from the same certified data foundation.

03

ETL/ELT Pipeline Development

Reliable data pipelines are the foundation everything else runs on. We build pipelines with production-grade standards: idempotent design so reruns do not create duplicates, full observability through Azure Monitor and custom alerting, data quality checks at ingestion that catch problems before they propagate, retry and dead-letter handling for transient failures, and lineage tracking so every dataset can be traced back to its source. The pipelines we build are maintainable by your team — clean code, documented logic, tested transformations — not black boxes that require the original developer to debug.

04

Cloud Migration from On-Premise

Migrating an on-premise data infrastructure to Azure requires more than lifting and shifting existing systems. Most on-premise data warehouses were built with architectural assumptions — fixed schema, batch processing, vertically scaled compute — that do not translate to cloud-native design. We assess your current environment, redesign for cloud-native patterns, and execute the migration in phases that maintain continuity for existing reporting and analytics consumers throughout. We plan parallel operations, validate data quality at each phase, and cut over only when the cloud environment is proven under real load.

05

Databricks & Snowflake Engineering

Deep expertise in the two dominant cloud data platforms. For Databricks: cluster configuration and autoscaling, Delta Lake table optimisation (OPTIMIZE, ZORDER, VACUUM), Unity Catalog access control, Spark job tuning, and MLflow for experiment tracking. For Snowflake: virtual warehouse sizing and query performance optimisation, Snowpipe for real-time ingestion, Dynamic Tables for materialised views, Iceberg Table configuration, and the Snowflake Data Sharing ecosystem. We also handle the common multi-platform architecture where Databricks serves ML workloads and Snowflake serves SQL analytics from the same underlying Delta/Iceberg data.

06

Cloud Cost Optimisation

Cloud data spend grows faster than anticipated in most enterprise environments. The root causes are structural: compute resources left running when not needed, queries scanning full tables when proper partitioning and clustering would eliminate 80% of the data scanned, storage retaining data that should be archived or deleted, and multiple copies of the same data in different tools without a clear ownership model. We audit your cloud data spend across compute, storage, and query patterns — identify the highest-cost structural issues — and restructure for material savings. Average cost reduction across our cloud optimisation engagements is 40%. The work typically pays for itself within 60 days.

When You Need Us

Use Cases

01/
You are migrating off on-premise infrastructure and need the cloud architecture right from the start
The decisions made in the first 90 days of a cloud data migration define the cost structure and performance ceiling for years. Organisations that lift-and-shift on-premise patterns to the cloud inherit on-premise problems at cloud prices. We assess your current environment, design for cloud-native architecture from the start, and execute a phased migration that keeps your existing reporting running throughout. The most common failure we prevent: attempting a big-bang migration and discovering mid-project that the source data is significantly more complex than the initial assessment assumed.
02/
Your Azure environment costs are growing but the analytics value is not keeping pace
Cloud data environments without active cost governance become expensive quickly. The pattern we consistently find: Databricks clusters running on schedules rather than demand, ADF pipelines running unnecessary refreshes, storage retaining full-fidelity data that should be archived at lower tiers, and query patterns that scan entire tables where proper partitioning would eliminate most of the compute cost. We audit your environment, identify the high-cost structural issues, and restructure. Cost reduction of 30–50% is achievable without reducing analytical capability.
03/
You need to build a modern data lakehouse that supports both BI reporting and AI workloads
The core architectural requirement for AI-ready infrastructure is that your analytics and AI workloads run from the same certified, governed data — not from separate copies with different quality standards. We design and build medallion architecture lakehouses where the Gold layer serves Tableau and Power BI reporting while the same Silver-layer data powers ML feature engineering and model training. When the data is the same, the AI outputs can be trusted against the same quality standards your analysts use. We have built this pattern for financial services, healthcare, and technology organisations — the implementation details differ by industry, but the architecture is consistent.
04/
Your data pipelines are unreliable and your team spends too much time fixing failures
Pipeline reliability is an engineering discipline, not a monitoring problem. Teams that spend significant time managing pipeline failures almost always have pipelines that were built without proper error handling, without idempotent design, and without data quality gates. Adding alerts on top of fragile pipelines does not fix the fragility — it just means you know about the failures faster. We rebuild fragile pipelines with proper engineering standards: idempotent logic, data quality validation at ingestion, proper retry handling, dead-letter queues for data that cannot be processed, and clear documentation of what each pipeline does and why.
05/
You are integrating operational technology (OT) data with your enterprise analytics platform
Energy, manufacturing, and industrial organisations face the specific challenge of connecting operational technology — SCADA systems, PI historians, MES platforms, IoT sensor feeds — with enterprise analytics and financial reporting systems. OT data is high-frequency, high-volume, and often structured very differently from the transactional data that enterprise data platforms were designed for. We design the ingestion architecture that bridges OT and IT: handling sensor data at appropriate granularity, building the aggregation layers that make operational data useful for financial reporting, and maintaining the data quality standards that regulated industries require.

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FAQ

Common Questions

What does an Azure data engineering engagement typically cost?

Cloud data engineering engagements run across a wide range depending on scope. A cloud architecture assessment (mapping your current state, identifying gaps, producing a remediation roadmap) typically costs $15,000–$35,000 for a mid-market organisation — 2–3 weeks of senior engineering time. A greenfield lakehouse build covering 3–5 data domains typically runs $80,000–$200,000. A full enterprise data platform build with 10+ data domains and complex governance requirements runs $200,000–$500,000+. Cloud migration engagements from on-premise infrastructure are scoped per environment — a mid-market organisation migrating a SQL Server data warehouse typically runs $60,000–$150,000. We provide fixed-price proposals for defined scope after an initial discovery call.

Should we use Databricks or Snowflake for our data platform?

Both are excellent platforms — the right choice depends on your primary workloads. Databricks is stronger when you have significant ML and AI engineering requirements, need Spark for large-scale distributed processing, or are building a lakehouse on Delta Lake with complex transformation requirements. Snowflake is stronger for SQL-first analytics teams, time-series workloads, and organisations that want a fully managed platform with minimal infrastructure overhead. Many enterprise organisations run both: Databricks for ML engineering and complex Spark pipelines, Snowflake for governed SQL analytics. If you are on Azure with a Microsoft 365 footprint, Microsoft Fabric is a third option that integrates tightly with your existing investments. We work across all three and will recommend based on your actual workload requirements.

What is dbt and do we need it?

dbt (data build tool) is a transformation framework that lets you write data transformations as SQL SELECT statements with built-in testing, documentation, and version control. It runs inside your data warehouse or lakehouse — it is not another data movement tool. For most organisations building a Silver and Gold layer in a lakehouse, dbt is the right tool for managing transformation logic: it makes transformations testable, documented, and maintainable. If your transformation requirements are simple and your team is SQL-first, dbt is almost always the right choice over custom Spark or Python. The main reason organisations do not use dbt is that they have not been introduced to it — it has a modest learning curve but pays back quickly in maintainability.

How long does a cloud data migration from on-premise take?

A mid-market organisation migrating a SQL Server or Oracle data warehouse to Azure typically takes 12–20 weeks for the full migration, including assessment, redesign, pipeline build, parallel operation, and cutover. The timeline is driven primarily by data complexity (number of source systems, data volume, transformation logic complexity) and governance requirements. The most common cause of delays is discovering mid-migration that source data quality is significantly lower than assumed — which is why we always start with a thorough assessment before committing to a migration timeline. Organisations that skip the assessment and go straight to migration routinely hit scope problems in weeks 6–10 that extend the timeline by months.

How do you handle data governance in a cloud data platform?

Governance in a cloud data platform requires three things: access control (who can see what data), data lineage (where does each dataset come from and what transformations has it gone through), and data quality (what standards does each dataset meet and who is accountable for maintaining them). On Databricks, Unity Catalog provides fine-grained access control at the column level, complete data lineage tracking, and a cataloguing interface for data discovery. On Snowflake, a combination of role-based access control and Dynamic Data Masking handles access governance. For organisations with regulatory requirements — HIPAA, PCI, SOC 2 — we design governance frameworks that satisfy audit requirements with documented lineage, access logs, and data classification policies. Governance is not a feature you add after the platform is built; it needs to be designed in from the start.

Can you support both our Azure environment and our Tableau or Power BI layer?

Yes. We work across both layers — the data platform and the BI layer — as integrated disciplines. The most common failure mode in enterprise analytics is building a technically sound data platform that is connected to BI tools poorly: wrong connection types (live connections to tables that should be extracts), no semantic layer between the Gold layer and the BI tool, and business logic duplicated independently in the BI tool rather than governed in the platform. We design the integration between your cloud data platform and your BI layer as a deliberate architecture decision, not an afterthought. See our data architecture consulting and Tableau consulting pages for more on how we approach both sides.

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