Turn Disorganised Data Into a Reliable Business Asset.
We help teams clean up, restructure, and operationalise their data infrastructure. ETL pipelines, data warehousing, governance frameworks, quality validation, migrations, and analytics enablement — handled end to end.
Data Management: Apps, Process, and Migrations.
Disorganised data costs businesses time and money. Reports take days to produce. Decisions are made on stale exports. Pipelines break silently. Teams distrust their own numbers. We fix the infrastructure that makes all of that happen.
From building ETL pipelines and data warehouses to migrating legacy databases and establishing governance frameworks, we handle the full spectrum of data engineering and management work. We leave you with a data infrastructure your team can trust and build on.
The problems we fix and what replaces them.
Most data problems follow recognisable patterns. Here is what we typically find and what we replace it with.
Reports built manually in spreadsheets
Each report is a copy-paste exercise. Numbers differ between teams. No single source of truth.
Automated warehouse with BI connectivity
One source of truth, refreshed on schedule, accessible to every team in their BI tool of choice.
Pipelines that break silently
Failures go unnoticed until someone spots wrong numbers. No alerting, no logging, no audit trail.
Monitored pipelines with alerting
Failures surface immediately via alert. Every run is logged with status, row counts, and duration.
Legacy database no one understands
Undocumented schema, orphaned tables, unknown relationships, and no migration path forward.
Documented, migrated, clean schema
Full schema documentation, data lineage maps, and a phased migration to a maintainable structure.
Data quality no one trusts
Duplicates, nulls, inconsistent formats, and no validation rules. Analysts spend 60% of their time cleaning data.
Validated data with quality contracts
Schema tests, freshness checks, referential integrity rules, and automated anomaly detection built into the pipeline.
Six areas of data management work we deliver.
Each engagement covers one or more of these areas. We scope exactly what is needed — not a broader transformation project than your situation requires.
ETL and ELT Pipeline Engineering
Design and implementation of extraction, transformation, and loading pipelines connecting your source systems to your warehouse or data lake. Includes scheduling, retry logic, monitoring, alerting, and run history logging.
Data Warehousing and Modelling
Warehouse setup on BigQuery, Redshift, Snowflake, or PostgreSQL. Dimensional modelling, dbt model development, mart and reporting layer design, and performance optimisation for analytical query patterns.
Database Migration
Structured migrations from MySQL to PostgreSQL, on-premise to cloud, monolith to microservices, or legacy schema to modern design. Schema audit, data mapping, transformation scripts, cutover planning, and rollback strategy.
Data Quality Frameworks
Data quality contracts, schema tests, freshness rules, referential integrity checks, and anomaly detection built into your pipeline. Automated quality reports and alerting when thresholds are breached.
Data Governance
Data catalogue setup, ownership assignment, access control policies, PII tagging and masking, audit trail implementation, and documentation frameworks so your team knows what data exists, where it comes from, and who can use it.
Analytics Enablement
BI tool integration (Looker, Metabase, Power BI, Superset), semantic layer setup, self-service reporting configuration, and dashboard builds so non-technical stakeholders can access trusted data without engineering support.
From data audit to production-ready infrastructure.
We do not drop a generic data framework into your business. We start by understanding exactly what you have, what is broken, and what the data needs to support.
Data audit and discovery
We map your current data sources, schemas, pipelines, storage, access patterns, and reporting needs. We identify quality issues, gaps, and risks before scoping any work.
Architecture and tooling design
We design the target architecture — sources, ingestion layer, warehouse, transformation models, and serving layer. Tool selection is driven by your scale, budget, and team skills.
Pipeline and model build
We build and test pipelines, dbt models, quality checks, and orchestration. Staged delivery so you can verify each layer before the next is added.
Quality validation and testing
Schema tests, freshness checks, row count reconciliation against source systems, and anomaly detection rules activated across all models before go-live.
Handover and documentation
Full documentation of every model, pipeline, governance policy, and operational runbook. Knowledge transfer session so your team can own and extend what we built.
The data stack we work with.
Brands Consulted
Projects Delivered
Years of Expertise
Transactions Processed
Common questions about our data management work.
Have a specific data problem in mind? Send it through the contact form and we will respond with a direct technical answer, not a sales call.
Talk to usETL (Extract, Transform, Load) transforms data before loading it into your destination, which made sense when warehouse compute was expensive. ELT (Extract, Load, Transform) loads raw data first and transforms it in the warehouse using tools like dbt, which is the modern standard now that warehouse compute is cheap. For most businesses today, ELT is the right approach — it preserves raw data, makes transformations transparent and testable, and integrates cleanly with dbt. We will recommend the right pattern based on your data volumes, source system constraints, and team capabilities.
We use a phased approach with a dual-write or CDC (change data capture) strategy to keep the target database in sync with the source during transition. The process covers schema audit and mapping, data type conversion, constraint and index recreation, application-layer compatibility testing, and a cutover window planned for your lowest-traffic period. We produce a detailed runbook and a tested rollback plan before any cutover happens. The goal is zero data loss and minimal application downtime, typically under 15 minutes for the final switch.
dbt (data build tool) is a transformation framework that lets you write SQL models, test them, document them, and deploy them with version control. It replaces ad-hoc transformation scripts with a structured, testable, and maintainable codebase for your analytics layer. If you have a data warehouse and want to build reliable, documented transformations on top of your raw data, dbt is the right tool. If you are at an earlier stage with simple reporting needs, you may not need it yet. We will give you an honest view of whether it is appropriate for your current situation.
We build data quality into the pipeline, not on top of it. This includes schema tests (not-null, unique, accepted-values), referential integrity checks across tables, freshness assertions that alert when data stops arriving on schedule, row count reconciliation against source systems after each run, and anomaly detection rules that flag unusual spikes or drops. When a quality check fails, the pipeline stops and alerts the right people before bad data reaches your dashboards or reports.
The right choice depends on your cloud provider, data volumes, query patterns, team skills, and budget. BigQuery is a strong default for GCP users, with serverless pricing and excellent performance on large scans. Redshift suits AWS-heavy organisations with predictable, high-volume workloads. Snowflake is cloud-agnostic and excels at multi-cluster concurrency and data sharing. For smaller data volumes or cost-sensitive situations, PostgreSQL or DuckDB may be sufficient and significantly cheaper. We assess your situation and make a concrete recommendation before any tooling decisions are made.
Yes. We connect Looker, Metabase, Power BI, Tableau, Apache Superset, and custom-built dashboards to the warehouse as part of the delivery. This includes configuring credentials and connections, setting up semantic layer definitions or Looker Explores where relevant, and building the first set of core dashboards so your team has working reports on day one rather than starting from a blank canvas.
