We design, migrate, and govern enterprise data platforms — from legacy warehouse modernisation and Lakehouse architecture to pipeline engineering, analytics enablement, and data governance frameworks built for long-term operational use.
Modern data challenges are no longer limited to reporting. Organizations today struggle with aging databases, rising cloud costs, inconsistent data quality, slow analytics, and platforms that cannot scale with business growth. We help enterprises modernize databases, migrate data platforms to the cloud, improve performance, and enable analytics — with ownership from assessment through stable operations. This is not a tools-first exercise; it is about building reliable, scalable, and cost-efficient data foundations.
Modernize legacy data warehouses and analytics systems into scalable, cloud-ready platforms. Includes warehouse modernization, cloud data platform architecture, and platform consolidation.
Design unified lakehouse platforms combining the flexibility of data lakes with the performance and reliability of data warehouses.
Build reliable batch and streaming pipelines that move data across systems with monitoring, validation, and operational stability.
Implement analytics platforms and reporting layers that transform enterprise data into consistent, trusted business insights.
Establish governance frameworks that ensure trusted data through ownership models, quality controls, and access governance.
Design master data frameworks that maintain consistent and authoritative records across enterprise systems.
AI-assisted schema mapping, anomaly detection, and lineage documentation help our data engineers cover more ground during migration and governance work — every output still reviewed by a senior practitioner.
See how AI supports our delivery →We take end-to-end ownership of data modernization initiatives — from assessment and design through execution and stable operations. Our focus is on delivering reliable, scalable, and cost-efficient data platforms that can be operated and evolved with confidence.
Auditing the existing data estate — source systems, data flows, warehouse architecture, data quality, and governance maturity — to define a risk-aware modernisation strategy.
Designing the target-state data platform — platform selection, zone structure, pipeline framework, and governance model — documented as reviewable blueprints.
Building data pipelines, transformation models, and BI foundations, with data quality checks embedded at every layer.
Standing up the analytics and BI layer, delivering runbooks and knowledge transfer so your team can operate independently.
Most data platforms underdeliver not because the technology was wrong, but because the architecture was undisciplined, governance was retrofitted, and the platform was designed for the proof-of-concept rather than the production operating environment. We build differently. Architecture before analytics. Governance built into the platform. Production-ready data foundations.
Platform selection, zone structure, table formats, and pipeline frameworks are decided and documented before a single pipeline is built. Architectural decisions made late are expensive to reverse.
Data ownership, quality rules, classification, and lineage are structural properties of the platform, enforced by design rather than audited after the fact.
A platform that stores data is infrastructure. A platform that delivers trusted, timely, accurately-modelled data is a competitive asset. We design every layer with the consuming use case in mind.
A data platform your team cannot operate, debug, and extend is a liability disguised as an asset. Every engagement leaves your data team genuinely capable, not continuously reliant on us.
Structured service areas — each with a defined scope, documented deliverables, and a senior data engineer accountable for outcome from discovery through production validation.
A structured evaluation of your existing data estate, producing a documented modernisation strategy with platform recommendation, prioritised roadmap, and TCO analysis.
End-to-end engineering of data pipelines — from ingestion through transformation and serving — with embedded data quality checks and operational runbooks.
Design and implementation of operationally enforced data governance frameworks, embedded into platform operations rather than maintained as separate documentation.
Design of the analytics layer — dimensional models, semantic layer, metric definitions — so business users receive consistent, trusted, governed data.
Data platform modernisation does not end at go-live. Production environments require structured pipeline operations, quality monitoring, and continuous improvement. Our managed services practice continues where implementation ends.
Ongoing operational ownership of database and data platform environments — performance monitoring, patching, and backup governance with defined SLAs.
SRE-led managed operations for data platform infrastructure — SLO tracking, capacity planning, and observability engineering.
Continuous security posture monitoring and compliance reporting across SOC 2, ISO 27001, HIPAA, PCI-DSS, and GDPR.
Building the next capability layer on your modernised platform — ML feature stores and AI-driven automation that depend on clean, governed data.
Connect with our team and define a clear, structured path forward for your data platform. Whether legacy warehouse migration, lakehouse architecture, or governance — we would be glad to collaborate.
A structured two to three week evaluation of your current data estate, producing a platform recommendation and modernisation roadmap.
An independent senior data architect review of your current or planned data platform, identifying design risks and governance gaps.
You speak with the engineer who would lead your engagement, not a pre-sales representative.
Data modernisation requires disciplined execution, documented architecture, and verifiable data quality at every stage. Every engagement produces a defined set of deliverables, accepted at each phase gate.
Ensuring that work is measurable, documented, and transferable to your team at engagement close.
No pipeline is built before the target-state architecture is reviewed, documented, and formally accepted by your team.
Data quality checks, row count reconciliation, and business rule validation are embedded, not applied as a post-build audit.
All pipeline code, dbt models, and configuration is version-controlled and deployable by your team independently.
Each phase has documented acceptance criteria, signed off before the next phase begins. No ambiguous completions.
Data accuracy is validated against business-defined expectations, not just technical row counts, before production certification.
Runbooks, architecture walkthroughs, and pipeline documentation sessions are formal deliverables, not optional extras.
Specific questions about your data platform, migration approach, or engagement scope? Our senior data engineers are ready to talk.
Many organisations begin with an assessment engagement — a structured evaluation of the current data estate that produces a platform recommendation, modernisation roadmap, and effort estimates before any major investment is committed.
Four stages: assessment (inventory, complexity scoring, dependency mapping), architecture design (target platform, schema translation, pipeline framework), migration execution (schema translation, pipeline replatforming, wave-by-wave data migration), and validation and cutover (business rule validation, report parity, production certification).
Depends on your use cases, data volumes, team capabilities, and governance requirements, not what's currently trending. We assess your specific situation and provide a documented recommendation with rationale.
Validation is embedded at every layer. We implement reconciliation frameworks that validate row counts, aggregate totals, and key field distributions before each table or pipeline is declared production-ready.
Every dataset has a documented owner, a defined purpose, a quality standard, and a known lineage. Data consumers find data through a catalogue rather than asking colleagues, and quality failures are alerted on automatically.
Yes. We review the current model structure, test coverage, and documentation standards, and either build on what exists or refactor where the architecture is creating downstream problems.
Based on the actual business requirement — how fresh the data needs to be, and at what cost. Streaming adds real complexity; for many use cases, micro-batch or hourly batch is sufficient and far simpler to operate.
A focused assessment takes two to three weeks. A small warehouse migration can complete in eight to twelve weeks. A large enterprise migration typically takes six to twelve months using phased, business-priority sequencing.