Data infrastructure the rest of the organisation can build on.

When data infrastructure is unreliable or undocumented, every analytics and ML project becomes slower. We build pipelines that are tested, monitored, and structured for the teams that maintain them.

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DATA PLATFORM

Pipeline Status

All Running

⬇️

Ingest

12 sources

📦

Stage

Raw layer

⚙️

Transform

dbt models

🚀

Serve

BI / APIs

Data Sources

↑ 12 active

PostgreSQL1,240 rows/s

Kafka Events892 rows/s

REST APIs3,401 rows/s

S3 Files201 rows/s

Salesforce644 rows/s

248

Tables

Models

1,892

Tests

01. Data Audit: Map your data sources, assess quality and completeness, and identify the gaps blocking downstream analytics and ML use cases.

02. Architecture Design: Warehouse structure, pipeline design, and tooling selection — documented and reviewed before any build work begins.

03. Ingestion Pipelines: Build ingestion from all sources — batch via Fivetran or custom ETL, streaming via Kafka or Kinesis — into the raw layer.

04. Transformation: dbt or Spark models transforming raw data into staging and mart layers. Data tests at every layer catch issues before they reach consumers.

05. Data Quality: Automated quality tests — null rates, referential integrity, volume anomaly detection, freshness checks — with alerts before SLAs are missed.

06. Serve & Monitor: Warehouse and lake served to BI tools, ML pipelines, and applications. Pipeline monitoring dashboard tracks every run.

How We Work

Every engagement follows defined phases — each delivering something concrete before we move forward.

01Data Audit

Data Audit

Map your data sources, assess quality and completeness, and identify the gaps blocking downstream analytics and ML use cases.

STAGE 1 OF 6

All Data Sources

Quality · Gaps · Owners

Audit Report

Ready

Source inventory

Quality scores

Gap map

What We Deliver

Specific capabilities and deliverables — built, tested, and handed over.

Ingestion, transformation, and loading pipelines built with Airflow, dbt, and the orchestration tools appropriate to your stack.

Batch & streamingIdempotent executionFull observability

Dimensional modelling, partitioning strategy, and query optimisation for Snowflake, BigQuery, and Redshift.

Dimensional modellingPartition strategyQuery optimisation

Real-time event processing with Kafka and Flink for use cases that cannot wait for batch refresh cycles.

Apache KafkaApache FlinkLate-event handling

Automated quality tests, volume anomaly detection, freshness monitoring, and alerting — catching issues before they reach consumers.

dbt testsGreat ExpectationsFreshness SLAs

Documentation of datasets, owners, and transformation lineage — so your team knows what exists and where it comes from.

Column-level lineageDataset ownershipFreshness docs

Technology Stack

We choose tools based on your requirements — not what is trending.

Ingestion

FivetranAirbyteStitchCustom Python

Transformation

dbt CoreApache SparkSQLPySpark

Streaming

Apache KafkaAWS KinesisApache Flink

Warehouses

SnowflakeBigQueryRedshiftDatabricks

Industries We Serve

Data Infrastructure applied across sectors.

Finance

Transaction data pipelines, regulatory data warehouses, real-time fraud feature stores.

Healthcare

Clinical data lakes, FHIR pipeline integration, population health data infrastructure.

Retail

Unified commerce data platform, real-time inventory pipelines, personalisation feature stores.

Telecom

Network event streaming, CDR processing pipelines, usage data warehouse.

See all industries

Frequently Asked Questions

Common questions about this service and what we hand over.

A warehouse stores structured, processed data optimised for querying. A lake stores raw data in any format — optimised for cost and flexibility. Lakehouses combine both. We recommend the right architecture for your actual use cases.

Yes, for most warehouse transformation work. dbt provides version control, documentation, testing, and lineage that makes transformation logic maintainable. We use it with your warehouse of choice.

With watermarks and late-event handling in Flink or Spark Structured Streaming. The tolerance window is defined based on your business rules and documented explicitly.

All pipeline code, dbt models, orchestration DAGs, data quality tests, architecture documentation, and runbooks. Your team can maintain and extend the infrastructure without us.

OUR APPROACH

Why not a generic agency?

The difference is not in the technology stack. It is in how the work is structured.

Spec before code

We write the contract, architecture document, or data model before a single line of implementation. You see exactly what will be built before we build it.