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Home / Services / Data Engineering

DATA
ENGI
NEERING

Service Category
Data & Analytics
Platforms
Snowflake · BigQuery · Databricks
Typical Engagement
2 – 10 months

We build the data infrastructure that transforms raw operational data into reliable, queryable intelligence — pipelines, warehouses, and analytics layers that your entire organisation can trust and act on.

Data Quality as a Foundation Bad data creates bad decisions. We instrument every pipeline with schema validation, freshness checks, and row-count assertions so your dashboards reflect reality — not silent failures upstream.
Real-Time & Batch Unified We architect data systems that serve both analytical batch workloads and real-time streaming use cases from a single platform — no duplicate pipelines, no synchronisation debt.
Self-Service Analytics The goal is always an organisation where analysts answer their own questions without waiting for engineering. We build the models, documentation, and tooling that make that possible.
What's included

WHAT WE
DELIVER

Data Warehouse Design

Dimensional modelling, star and snowflake schemas, and semantic layer design on Snowflake, BigQuery, or Redshift. Built for query performance and analytical flexibility from day one.

ETL / ELT Pipeline Development

Batch and streaming pipelines using dbt, Fivetran, and custom Spark jobs. Incremental loads, CDC patterns, and idempotent transformations that make reruns safe and cheap.

BI & Analytics Platforms

Looker, Metabase, and Tableau implementations with governance models, certified metric definitions, and embedded analytics that put decision-relevant data directly in your product.

Real-Time Streaming

Kafka and Flink pipelines for event-driven analytics, fraud detection, and operational dashboards. Sub-second latency from event emission to business intelligence — no batch delay tolerated.

Data Lakehouse Architecture

Delta Lake, Apache Iceberg, and Hudi table formats on S3 or GCS — giving you warehouse-grade ACID transactions over your data lake without the lock-in of a managed warehouse.

Data Quality & Observability

Great Expectations and Monte Carlo integration for automated data quality testing, anomaly detection, and lineage tracking across every table in your warehouse. Know when data breaks before your users do.

Tools & platforms

OUR
STACK

Warehouse
Snowflake
Warehouse
BigQuery
Warehouse
Databricks
Transform
dbt
Ingestion
Fivetran / Airbyte
Streaming
Apache Kafka
Processing
Apache Spark
Orchestration
Airflow / Prefect
BI
Looker / Metabase
Quality
Great Expectations
Lake Format
Delta Lake / Iceberg
Observability
Monte Carlo
Featured work

CASE
STUDY

Postgres CDC
Event Stream
Kafka
dbt / Spark
Snowflake
Looker
Data / SaaS
Trackou Unified Analytics Platform

Trackou had data scattered across six SaaS tools with no single source of truth. We designed a Snowflake data warehouse, built automated dbt pipelines from every source, and delivered a Looker semantic layer that cut analyst query time from hours to seconds.

6
Sources Unified
<30s
Dashboard Refresh
100%
Pipeline Uptime
Common questions

FAQ

We already have a data warehouse. Can you improve it? +
Yes. We start with an audit covering data quality, pipeline reliability, query performance, and cost efficiency. Most mature warehouses have significant optimisation headroom — we typically find 40–70% cost reduction opportunities and numerous reliability improvements within the first two weeks.
How do you handle sensitive or regulated data? +
We implement column-level encryption, row-level security policies, dynamic data masking, and comprehensive audit logging from the start. Our architectures are designed to support GDPR, HIPAA, and PCI-DSS compliance requirements, and we can work with your DPO throughout the project.
What does a typical data platform build timeline look like? +
A foundation sprint (warehouse setup, first two or three source integrations, core dbt models, and a basic BI layer) typically takes 6–8 weeks. Each subsequent data source integration runs 1–3 weeks depending on API complexity and data quality. We deliver value incrementally — you're not waiting months for a big bang.
How do you keep our data warehouse costs under control? +
We implement query result caching, materialisation strategies (incremental over full refreshes wherever possible), warehouse auto-suspend policies, and a tagging framework for cost attribution. Every project includes a cost dashboard so you can see exactly what each pipeline and dashboard is spending.
Can business analysts use the platform without SQL knowledge? +
That's explicitly one of our goals. We build a well-documented semantic layer — either in Looker or a dbt metrics layer — that exposes business-friendly dimensions and measures. Analysts explore data through a BI tool without writing raw SQL. We also run onboarding sessions to make your team independent from day one.
Ready to build

START YOUR
PROJECT

Tell us about the workflows you want to automate and we'll map a path from where you are to where you want to be.

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