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Data Engineering & Business Intelligence

Reliable data platforms and self-serve reporting teams actually use.

SourcesStreaming /batch ETLWarehousedbt semanticlayerDashboardsTypical system shape for this service

Signs you need this

  • Three departments report three different numbers for the same metric.
  • Nightly jobs fail silently and someone finds out at the Monday meeting.
  • Your BI tool licence costs more than the insight it delivers.

Overview

Conflicting spreadsheets, nightly jobs that fail silently and metrics defined three different ways cost more than most companies realise. A modern data platform fixes that with one pipeline, one model layer and one place to look.

We build streaming and batch pipelines, warehouse and lakehouse architectures, dbt-based semantic layers and BI dashboards, and we migrate legacy BI stacks without unnecessary data movement.

How this differs from Data Science Solutions: Data Engineering & BI builds the pipelines, warehouse and dashboards; Data Science builds models on top of them.

What we deliver

  • Streaming & batch pipelinesKafka, CDC and Airflow-orchestrated ETL with alerting and lineage.
  • Warehouse & lakehouse designSnowflake, BigQuery, Redshift and Delta Lake with Kimball or Data Vault modelling.
  • Semantic layerdbt models that define each metric once and test it continuously.
  • DashboardsPower BI, Tableau, Superset, Looker or custom HTML dashboards for executives and teams.
  • BI migrationMove off legacy tools onto a modern, cheaper stack with parity checks.
  • Data quality & governanceTests, SLAs, access controls and compliance-ready architectures.

Where it fits

Unified reporting

Sales, operations and finance on one platform with shared definitions.

Real-time operations

Live event streams feeding dispatch, monitoring or fraud dashboards.

Analytics foundations for AI

Clean, versioned data that ML and RAG systems can trust.

Regulated data platforms

Multi-region designs meeting HIPAA, DPDP or similar requirements.

Technology we use

Chosen per project. We are vendor-neutral and will recommend what fits your constraints.

Apache KafkaAirflowdbtSparkDatabricksSnowflakeBigQueryRedshiftDelta LakeTimescaleDBPower BITableauApache SupersetLooker

How we work

Inventory

Sources, consumers, current pain points and required freshness.

Architecture

Target platform, modelling approach and migration path.

Build

Pipelines, models and dashboards delivered in increments with tests.

Adoption

Training, documentation and a feedback loop with report consumers.

Typical first engagement

A data-platform assessment covering sources, consumers and metric definitions, with a migration or modernisation plan.

Related work

Enterprise Data & Analytics

Insight Board

BI modernisation & unified reporting

Migrated a legacy BI stack to Apache Superset over an existing MySQL/PostgreSQL warehouse, unifying sales, operations and finance reporting. dbt models standardised metric definitions across departments; Airflow keeps dashboards refreshed nightly.

Outcome One self-serve platform replacing conflicting departmental spreadsheets, without moving the data.
Apache SupersetdbtAirflowPostgreSQL
AI Infrastructure / Enterprise Search

InfoBridge AI

Enterprise document intelligence & RAG assistant

Retrieval-augmented assistant giving employees natural-language access to policy manuals, contracts and wikis. Self-hosted Weaviate cluster (55M+ objects, 39 collections) with RQ-8 quantisation, a Kafka CDC pipeline for delta updates, and Memcached in front of frequent queries.

Outcome ~4x vector compression at 85–92% recall; low median latency under concurrent load.
WeaviateKafka CDCMemcachedRAG
Read the case study

Case studies

All projects

Common questions

Do we have to move to a new warehouse?

Not necessarily. We often modernise the BI and modelling layers over the database you already run.

How do you keep metrics consistent?

A dbt semantic layer where each metric is defined once, tested and reused by every dashboard.

Can you handle real-time data?

Yes: Kafka-based streaming and CDC pipelines for use cases where nightly batches are too slow.

Related services

Data Science Solutions

Forecasting, churn, scoring and recommendation models that ship.

Statistical Solutions Development

Experiment design, hypothesis testing, survival and cohort analysis.

Quantitative Finance Solutions

Strategy research, backtesting, risk and execution systems.

Ready to discuss Data Engineering & Business Intelligence?

A data-platform assessment covering sources, consumers and metric definitions, with a migration or modernisation plan.

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