A Future-Proof Data Platform — Reliable Pipelines, Clean Metrics, Faster Insights.

Scalable data engineering and cloud architecture on AWS, Azure, or GCP — ETL/ELT, streaming, governance and integration so your team can focus on analysis, not plumbing.

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NO OBLIGATION • TECHNICAL AUDIT INCLUDED

Example Data Ingestion Pipeline

Problem

Data is fragmented across systems, pipelines break at peak times, and analysts waste time cleaning messy datasets. Your dashboards are slow, production jobs fail, and governance is unclear — slowing decisions and increasing risk. Sound familiar?

Solution

We design and implement resilient, secure, and maintainable data platforms that make data reliable, discoverable and fast. From event streaming and ETL/ELT to our architecture work removes technical debt and builds a foundation that scales with your business.

Results

Reliable Data

Fewer pipeline failures, consistent daily refreshes, and trusted metrics.

Faster time-to-insight

Performant queries and prepared datasets for analysts and ML teams.

Scalable & compliant architecture

Scalable & compliant architecture — cloud-native patterns and governance to support growth and audits.

Clients

★★★★★

Trusted by engineering and analytics teams across startups and mid-market companies.

“Built for real-time analytics and quick decision-making.”

“Professional, transparent & empowering hand-over.”

“From messy spreadsheets to streamlined data flows.”

“A rock-solid foundation for all our data.”

"They rebuilt our data platform — jobs are stable, query times halved and our analysts can finally ship dashboards." — Head of Analytics, anonymized client.

Benefits

Streaming & batch ingestion (Kafka, Pub/Sub, Kinesis) — near real-time data for dashboards and alerting.
Data quality & observability (tests, monitoring, alerts) — catch upstream issues early and reduce firefighting.
Cloud-native reference architectures (AWS/Azure/GCP) — future-proof deployments that scale and match your security posture.
Secure storage & cost optimisation (lakehouse, warehouse, tiering) — control storage costs while keeping performance for critical workloads.
Data modelling & semantic layer (star schemas, dbt models, feature stores) — single source of truth for business metrics and ML features.
Scalable transformations (Spark, dbt, Dataflow) — maintainable pipelines and versioned transformations for reproducible metrics.
CI/CD & Infrastructure as Code (Terraform, GitOps) — predictable deployments and traceable changes.
Governance & compliance (access controls, lineage, PII handling) — ready for audits and regulatory requirements.
Handover & enablement — docs, runbooks and training so your team owns the platform post-delivery.

Streaming & batch ingestion (Kafka, Pub/Sub, Kinesis) — near real-time data for dashboards and alerting.
Data quality & observability (tests, monitoring, alerts) — catch upstream issues early and reduce firefighting.
Cloud-native reference architectures (AWS/Azure/GCP) — future-proof deployments that scale and match your security posture.
Secure storage & cost optimisation (lakehouse, warehouse, tiering) — control storage costs while keeping performance for critical workloads.
Data modelling & semantic layer (star schemas, dbt models, feature stores) — single source of truth for business metrics and ML features.
Scalable transformations (Spark, dbt, Dataflow) — maintainable pipelines and versioned transformations for reproducible metrics.
CI/CD & Infrastructure as Code (Terraform, GitOps) — predictable deployments and traceable changes.
Governance & compliance (access controls, lineage, PII handling) — ready for audits and regulatory requirements.
Handover & enablement — docs, runbooks and training so your team owns the platform post-delivery.

Mini Case Study

Case

SaaS scaleup — central data platform build. Fragmented pipelines, expensive BI queries, long incident triage.

Work Delivered

Single ingestion layer, dbt transformation layer, Snowflake data warehouse (or equivalent), DAG orchestration, monitoring & runbooks.

Outcome

Pipeline failure rate down 85%, average dashboard query time reduced 50%, weekly reporting time cut from 8 hours to 2 hours.

Architecture diagram, sample DAG screenshot, observability dashboard screenshot.

All Case Studies

More Data Ingestion Pipeline Examples

Pricing

Starter Pipeline — £4,000 (one-off)

Single ingestion pipeline + basic transformations + monitoring + 2 weeks support.

Best for validating an ETL/ELT workflow.

Apply

Platform —£15,000 (Project) — Most Popular

Full data platform (ingest, transform, warehouse/lakehouse, CI/CD, monitoring, governance), onboarding & 1 month support.

Recommended for teams moving from spreadsheets to a governed data stack.

Apply

Managed Platform — £3,000 / month (retainer)

Ongoing platform SRE, incident management, cost optimization, feature work and monthly runback reviews.

Best for teams that want to outsource platform ops.

Apply

Book free discovery call to secure priority delivery and access starter pricing.

LIMITED ONBOARDING SLOTS.

Guarantee of Work Assurance

SATISFACTION PROMISE.

We’ll deliver an agreed MVP pipeline or architecture review within the timeline in the Statement of Work. If agreed milestones are missed, we’ll propose corrective action and credit work time toward remediation. Every project includes a 30-day hypercare window after launch.

Book Consultation

NO OBLIGATIONS • NO TAILORED ROADMAP • FAST NEXT STEPS

How long does a typical engagement take?

Small-to-medium initial builds take 4–10 weeks. Larger platform builds or migrations depend on scope and data sources.

Do we need cloud expertise in-house?

We deliver with minimal vendor lock-in and hand over code, docs and runbooks. We can upskill your SRE / data engineers during the project.

Which stack should we choose — lakehouse, warehouse, or hybrid?

We recommend based on query patterns, cost targets, latency needs, and your team’s skills — and outline trade-offs in the discovery.

How do you handle sensitive data / PII?

We design role-based access controls, encryption-at-rest/in-transit, masking and lineage so compliance is built in.

What happens if our team needs ongoing support?

We offer retainer-based platform support & SRE services or a knowledge-transfer-only handover — whichever you prefer.

How long does a typical engagement take?

Small-to-medium initial builds take 4–10 weeks. Larger platform builds or migrations depend on scope and data sources.

Do we need cloud expertise in-house?

We deliver with minimal vendor lock-in and hand over code, docs and runbooks. We can upskill your SRE / data engineers during the project.

Which stack should we choose — lakehouse, warehouse, or hybrid?

We recommend based on query patterns, cost targets, latency needs, and your team’s skills — and outline trade-offs in the discovery.

How do you handle sensitive data / PII?

We design role-based access controls, encryption-at-rest/in-transit, masking and lineage so compliance is built in.

What happens if our team needs ongoing support?

We offer retainer-based platform support & SRE services or a knowledge-transfer-only handover — whichever you prefer.

How long does a typical engagement take?

Small-to-medium initial builds take 4–10 weeks. Larger platform builds or migrations depend on scope and data sources.

Do we need cloud expertise in-house?

We deliver with minimal vendor lock-in and hand over code, docs and runbooks. We can upskill your SRE / data engineers during the project.

Which stack should we choose — lakehouse, warehouse, or hybrid?

We recommend based on query patterns, cost targets, latency needs, and your team’s skills — and outline trade-offs in the discovery.

How do you handle sensitive data / PII?

We design role-based access controls, encryption-at-rest/in-transit, masking and lineage so compliance is built in.

What happens if our team needs ongoing support?

We offer retainer-based platform support & SRE services or a knowledge-transfer-only handover — whichever you prefer.