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.
Batch · Streaming · CDC · API
Data Lake · Lakehouse · Warehouse
dbt · Spark · SQL · ELT pipelines
BI · ML Features · APIs · Reporting
Catalog · Lineage · Quality · Access
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 designed for modern data workloads.
Includes
Design unified lakehouse platforms that combine the flexibility of data lakes with the performance and reliability of data warehouses.
Includes
Build reliable batch and streaming pipelines that move data across systems with monitoring, validation, and operational stability.
Includes
Implement analytics platforms and reporting layers that transform enterprise data into consistent, trusted business insights.
Includes
Establish governance frameworks that ensure trusted data through ownership models, quality controls, and access governance.
Includes
Design master data frameworks that maintain consistent and authoritative records across enterprise systems.
Includes
We take end-to-end ownership of data modernization initiatives — from assessment and design through execution and stable operations.
Our focus is not limited to upgrading databases or enabling analytics, but on delivering reliable, scalable, and cost-efficient data platforms that can be operated and evolved with confidence.
We work with clear scope, defined accountability, and measurable outcomes, ensuring modernization efforts translate into real performance, cost, and insight improvements.
Auditing the existing data estate — source systems, data flows, warehouse architecture, data quality, governance maturity, and analytics consumption patterns — to define a risk-aware modernisation strategy with documented priorities and sequencing logic.
Designing the target-state data platform — platform selection, lakehouse or warehouse architecture, zone structure, pipeline framework, governance model, and access control design — documented as reviewable blueprints before any implementation begins.
Building data pipelines, transformation models, data catalogue integrations, and BI foundations — with data quality checks embedded at every layer, and a validation framework that confirms data accuracy before each component is declared production-ready.
Standing up the analytics and BI layer, delivering operational runbooks, governance documentation, and data team knowledge transfer — ensuring your data organisation can operate, maintain, and evolve the platform without continued dependence on external support.
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.
Platform selection, zone structure, table formats, modelling conventions, access control patterns, and pipeline frameworks are decided — and documented — before a single pipeline is built. Architectural decisions made late are expensive to reverse. We make them early, deliberately, and with full understanding of their downstream implications for governance, performance, and cost.
Data governance is not a layer applied after the platform is built — it is embedded into the platform's architecture from the start. Data ownership, quality rules, classification, and lineage are structural properties of the platform, enforced by design rather than managed by a separate governance team attempting to audit an ungoverned system after the fact.
A data platform that stores data is infrastructure. A data platform that delivers trusted, timely, accurately-modelled data to the business is a competitive asset. We design every layer — ingestion, storage, transformation, serving — with the consuming use case in mind, ensuring that the platform's output is data that business users, analysts, and ML systems can rely on without verification rituals.
A data platform your team cannot operate, debug, and extend is a liability disguised as an asset. Every engagement is designed with operational sustainability in mind — clear naming conventions, documented pipeline logic, runbook coverage, observable data quality, and knowledge transfer that leaves your data team genuinely capable rather than continuously reliant on external support to keep the lights on.
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 — source systems, pipelines, warehouse architecture, governance maturity, and analytics consumption patterns — 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 — using modern frameworks, with embedded data quality checks, lineage documentation, and operational runbooks for every pipeline delivered.
Design and implementation of operationally enforced data governance frameworks — data cataloguing, classification, ownership assignment, quality rule implementation, and lineage tracking that are embedded into platform operations rather than maintained as separate documentation exercises.
Design of the analytics layer — dimensional models, semantic layer implementation, metric definitions, and BI environment deployment — ensuring that business users receive consistent, trusted, governed data without requiring direct access to the raw data platform.
Data platform modernisation does not end at go-live. Production environments
require structured pipeline operations, quality monitoring, performance management,
and continuous improvement. Our managed services practice continues where implementation ends.
Ongoing operational ownership of database and data platform environments — performance monitoring, query optimisation, patching, backup governance, and incident response with defined SLAs.
SRE-led managed operations for data platform infrastructure — SLO tracking, capacity planning, incident coordination, and observability engineering for cloud-native data environments.
Continuous security posture monitoring, vulnerability management, access control governance, and compliance reporting across SOC 2, ISO 27001, HIPAA, PCI-DSS, and GDPR.
Building the next capability layer on top of your modernised data platform — ML feature stores, model serving infrastructure, and AI-driven automation workflows that depend on clean, governed, trusted data.
A structured two to three week evaluation of your current data estate — producing a platform recommendation, modernisation roadmap, and documented effort estimates.
An independent senior data architect review of your current or planned data platform architecture — identifying design risks, governance gaps, and structural improvement opportunities.
You speak with the engineer who would lead your engagement — not a pre-sales representative. Every conversation is technically grounded, immediately relevant, and without obligation.
Data modernisation requires disciplined execution, documented architecture, and verifiable
data quality at every stage. Every engagement produces a defined set of technical and operational deliverables
— accepted at each phase gate, not claimed at engagement close.
Concrete data engineering and operational outputs delivered throughout the engagement lifecycle — documented, tested, and accepted against defined quality criteria.
Delivery governance applied across every data modernisation engagement — 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 documented — 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. This gives you a documented foundation for decision-making before any major investment is committed.
A data warehouse migration typically spans four stages: assessment (inventory of source tables, ETL jobs, and reports; complexity scoring; dependency mapping), architecture design (target platform selection, schema translation approach, ELT pipeline framework, and validation methodology), migration execution (schema translation, pipeline replatforming, and wave-by-wave data migration with reconciliation), and validation and cutover (business rule validation, report parity verification, and production certification before decommissioning the legacy system). Duration varies significantly based on warehouse complexity, data volume, and the number of downstream consumers — we scope this precisely during assessment.
The right architecture depends on your use cases, data volumes, team capabilities, and governance requirements — not on what’s currently trending. Organisations with primarily structured data, BI-focused consumers, and a need for strong query performance often benefit from a modern cloud data warehouse. Organisations with mixed structured and unstructured data, ML workloads, or a need to store raw data before schema decisions are finalised often benefit from a lakehouse. We assess your specific situation and provide a documented recommendation with rationale — not a platform-agnostic non-answer.
Data quality validation is embedded at every layer — not applied as a post-build check. During migration, we implement reconciliation frameworks that validate row counts, aggregate totals, and key field distributions against the source system before each table or pipeline is declared production-ready. Business rule validation — confirming that the data matches what the business expects, not just what the source system contains — is a formal acceptance criterion for each migration wave. Where data quality issues exist in the source, we document them explicitly rather than migrating them silently into the new platform.
Practically, data governance means that every dataset has a documented owner, a defined purpose, a quality standard, and a known lineage. It means data consumers can find data through a catalogue rather than asking colleagues. It means data quality rules are automatically enforced and failures are alerted on — not discovered when a report produces unexpected numbers. We implement governance as operational infrastructure — catalogue deployment, quality monitoring pipelines, access control policies, and stewardship workflows — not as a documentation exercise or a set of policies nobody reads.
Yes. We engage with existing dbt projects — reviewing the current model structure, test coverage, documentation standards, and deployment patterns — and either build on top of what exists or refactor where the architecture is creating downstream problems. Common issues we address in existing dbt projects include insufficient testing, over-reliance on models without clear documentation, inconsistent naming conventions, and performance problems caused by unoptimised model dependencies. We extend your setup rather than replacing it unnecessarily.
We design based on the actual business requirement — specifically, how fresh the data needs to be at the consuming layer and at what cost. Streaming adds significant operational complexity, cost, and maintenance overhead. For many use cases, micro-batch or hourly batch pipelines are sufficient and far simpler to operate reliably. Where genuine real-time requirements exist — event-driven applications, fraud detection, operational dashboards — we design streaming pipelines using Kafka, Flink, or cloud-native streaming services with appropriate error handling, backpressure management, and observability built in from the start.
Timeline depends on the complexity of the source warehouse, the number of tables and ETL jobs being migrated, and the downstream consumer count. A focused assessment takes two to three weeks. A small warehouse migration (under 100 tables, limited ETL complexity) can be completed in eight to twelve weeks. A large enterprise warehouse migration is typically delivered in six to twelve months using phased waves with business-priority sequencing. We produce a detailed timeline with milestones during the assessment phase — based on actual complexity analysis, not optimistic assumptions.
Testimonials
Pricing
Single Project
Single Prost
Portfolio
