Home / Practice / Data Engineering
— Practice 02 · Pipelines

Pipelines that don't break
at 3am,
and tell you when they do.

Schema-aware, observable, idempotent. We build the data movement layer between your source-of-truth systems and the platforms, models, and reports that depend on them, with tests on every hop.

// 01
120+
Pipelines in production
// 02
99.9%
Avg. pipeline SLA across managed estate
// 03
10B+
Rows processed per day across customers
// 04
< 5min
P95 incident detection time
— The thesis

Pipelines as code.
Data as a product.

Data engineering is the foundation that science, analytics, and ML are built on. Without proper engineering practices, organisations face bottlenecks, delays, inaccuracies, and missed opportunities — pipelines that break silently, dashboards that disagree, models trained on yesterday's data.

We treat data movement as software: version-controlled, peer-reviewed, tested in CI, deployed through pipelines, observed in production. Producers can't accidentally break consumers. Schema changes are explicit. Failures are loud.

The boring outcome: data arrives on time, in the shape you expected, and when it doesn't, you know inside five minutes and have a runbook on hand.

— What's inside

Anatomy of a pipeline

Every pipeline we ship has the same five concerns. Skip one, and it'll be the one that wakes you at 3am. So we don't skip them.

// 01

Source & extract

Reliable extraction from operational DBs, SaaS APIs, files, and event streams, with rate-limits, retries, and watermarking baked in.

Kafka Confluent Debezium Fivetran DLT ADF
// 02

Land & conform

Raw landing zone, schema validation, and conformance into the bronze tier. Late-arriving data and CDC events handled cleanly.

S3 + Iceberg Delta Lake OneLake Redshift Synapse
// 03

Transform & model

Silver and gold layers with dbt / Spark. Idempotent, deterministic, peer-reviewed. Every transformation has tests; every test runs in CI.

Spark dbt Airflow Glue Flink
// 04

Test & validate

Data contracts at the boundary, expectations on every model, and freshness / volume / distribution checks that fail loudly when violated.

Unity Catalog Purview Lake Formation Schema Registry
// 05

Orchestrate & observe

Airflow / Workflows / ADF with retries, SLAs, lineage, and metrics — wired into PagerDuty, Slack, or wherever your on-call lives.

Power BI Lakeview QuickSight Feast FastAPI
— Capabilities

What we actually do

Six engineering capabilities, mapped to where most pipelines fail. Most engagements start with ingestion and orchestration — that's where the silent breakage usually lives.

// 01

Ingestion & CDC

Move data without rewriting your source-of-truth. Batch, streaming, and CDC patterns with proper backpressure and replay.

  • Debezium / Connect / Fivetran
  • Watermarking & replay
  • Event-driven & pull-based
// 02

Transformation in dbt

SQL-first transformations with version control, peer review, and CI testing. Semantic models that downstream tools can trust.

  • dbt Core / Cloud projects
  • Medallion modelling
  • Macros & reusable patterns
// 03

Spark & distributed compute

When SQL alone won't do it — Spark on Databricks, EMR, or Glue. Tuned, partitioned, and observed.

  • PySpark / Scala jobs
  • Cluster sizing & autoscaling
  • Performance tuning & AQE
// 04

Streaming & CEP

Real-time pipelines on Kafka / Flink / ksqlDB. Stateful processing, windowed aggregates, and CEP patterns done properly.

  • Kafka / Confluent Cloud
  • Flink stateful streaming
  • Schema Registry & contracts
// 05

Orchestration & CI/CD

Pipelines deployed like applications, branch builds, automated tests, blue/green releases, and rollback on red.

  • Airflow / Workflows / ADF
  • GitHub Actions / Azure DevOps
  • Infra-as-code (Terraform)
// 06

Quality & observability

Data contracts, expectations, and freshness / volume / lineage observability. Pipelines that fail loudly, with context.

  • Great Expectations / dbt tests
  • Lineage (Unity, OpenLineage)
  • Alerting via PagerDuty / Slack
— Platforms

The technologies we build on.

The tools we reach for most. Always picked for the workload, never for the brochure.

DBT

dbt

SQL transformations & modelling

The de-facto standard for SQL-first transformations. Tests, docs, lineage, and CI all in one place. dbt Core or Cloud, both fine.

AF

Apache Airflow

Orchestration & scheduling

Self-hosted or MWAA / Cloud Composer / Astronomer. We treat DAGs like application code, version-controlled, tested, deployed via CI.

SP

Apache Spark

Distributed compute

On Databricks, EMR, Glue, or Synapse Spark. PySpark or Scala. Tuned, partitioned, observed. The hammer for big-data workloads.

KF

Kafka & Flink

Streaming & CEP

Confluent Cloud or self-managed. Streaming pipelines, CDC backbones, and stateful Flink jobs for real-time decisioning.

— How we engage

Three ways to start

Three shapes of engagement, depending on whether you need an opinion, a delivery team, or someone to keep the lights on.

// 01 / Consult

Pipeline audit

Health-check on your existing pipelines — failure modes, cost, lineage, test coverage. Output: a prioritised list of what's brittle and what to fix first.

  • Failure-mode analysis
  • Cost & performance review
  • Test & observability gaps
  • Prioritised remediation plan
// 02 / Build

Pipeline delivery

Engineers embedded with your team, shipping pipelines in fortnightly sprints. Knowledge-transfer is part of the deal — we leave the team better than we found it.

  • Greenfield pipeline builds
  • Migrations off legacy ETL
  • dbt / Airflow rollouts
  • Streaming & CDC implementations
// 03 / Run

Managed pipelines

We run your pipelines for you — SLAs, on-call, incident response, and continuous improvement. The cheapest way to get a dependable data team.

  • Pipeline operation & SLAs
  • Incident response, 24/7
  • Cost & performance reviews
  • Quarterly optimisation roadmaps
— A typical engagement

From nothing to shipped.

Most pipeline engagements run on this rhythm. We've shipped this exact shape for retail banks, insurers, and a continental telco.

STEP 01 ·

Map & scope

Audit existing pipelines, map sources and targets, identify priority data products. Output: a one-page roadmap and target architecture.

→
STEP 02 ·

Scaffold & standards

Repo structure, CI/CD, dbt project, orchestrator, observability, alerting. The plumbing every pipeline will inherit.

→
STEP 03 ·

Build & ship

Pipelines in fortnightly sprints, peer-reviewed by your engineers. Each sprint ends with a measurable outcome in production.

→
STEP 04 ·

Operate & evolve

Hand over, run alongside, or run for you. Pipelines stay operable either way — and the team stays sharp.

— Managed service

Business Insights Engine

Our managed data solution that operates your end-to-end pipelines, multi-source ingestion, transformation, quality, and report performance, all on an SLA.

We believe every business, regardless of size, should be able to leverage its data. The Insights Engine makes that possible at a fraction of the cost of building and retaining an in-house data engineering team.

  • Multi-source ingestion
  • Pipeline operation & SLAs
  • BI & report performance
  • Cost & performance reviews
  • On-call & incident response
  • Quarterly platform roadmaps
// platform · live
Insights Engine
INGEST
PIPELINE
QUALITY
BI
// SLA 99.9% REGION · ZA / UK
— Signature engagement

Streaming fraud signals
for a tier-1 retail bank.

Confluent Kafka + Flink streaming the transaction firehose into a fraud-decisioning service. Sub-200ms p95, with full lineage from auth event to scored decision.

/// outcome

10K events/s, <200ms p95, zero data loss.

Stateful Flink jobs joining auth events with feature pipelines and rules. Schema Registry contracts, dead-letter queues, and replay-from-offset on every consumer. 18 months in production with zero data-loss incidents.

10K/s
Throughput
< 200ms
P95 latency
0
Data-loss events
BRONZE SILVER GOLD SERVE
— Bring us a pipeline problem

Bring us the workload.
We'll bring the pipeline for it.

Pipeline audit, greenfield build, or full managed operation — start with a 30-minute call.

Book a pipeline call See selected work