Mach Lilies is a data consultancy that builds the pipelines, warehouses and analytics that turn raw data into decisions — and the clean, grounded foundation your AI agents and models need to work. From real-time ingestion to dashboards people trust. Mach speed. Lily craft.
Raw data is noise. We turn it into insight you can act on — reliably.
Data engineering builds the plumbing — pipelines, warehouses and models — that makes analytics and AI possible; analytics turns that data into decisions. As a senior data practice, Mach Lilies designs modern data platforms (Snowflake, BigQuery, Databricks), builds trustworthy pipelines, and delivers analytics people actually use — including the AI-ready, well-grounded data foundation that retrieval, agents and ML depend on.
Pipelines, warehouses, analytics and governance — engaged together or à la carte.
Reliable batch and streaming pipelines (ELT) you can trust to be correct and on time.
Modern warehouses and lakehouses — Snowflake, BigQuery, Databricks — modeled well.
Dashboards and self-serve analytics people actually use to make decisions.
Event pipelines for sub-second insight and operational analytics.
Quality, lineage, cataloguing and governance that scale with the organisation.
The clean, well-modeled, well-grounded data foundation that AI agents and ML actually need.
Four movements from scattered data to a platform that drives decisions and AI.
We map sources, quality and the questions the business actually needs answered.
A warehouse and data model built for trust, performance and change.
Tested pipelines with quality checks, so the numbers are right every time.
Analytics, dashboards and the data foundation your ML and AI agents build on.
We build data platforms for regulated, high-volume domains — startups and enterprises alike.
Principals design and build your data platform — no hand-off to juniors.
dbt, Snowflake, BigQuery, Databricks — the proven modern data stack, done right.
We start from the decisions you need to make, then build backward to the data.
Documented, tested, governed. Your team owns and trusts the platform.
Data you can trust and act on — the foundation your AI needs.
Data engineering is the design and build of the systems that collect, move, store and prepare data — pipelines, warehouses and models — so it's reliable and ready for analytics and AI. It's the foundation everything data-driven stands on.
Data engineering builds the trustworthy data foundation; analytics (and BI) turns that data into insight and decisions. We do both, so the two fit together cleanly.
It depends on your scale, ecosystem and team. We're neutral across Snowflake, BigQuery and Databricks and will recommend the best fit rather than a default.
Most reporting is well served by batch. Real-time matters when decisions are operational and time-sensitive. We'll recommend the simplest approach that meets the need.
With testing, validation, monitoring and lineage built into the pipelines (using tools like dbt) — so issues are caught early and the numbers can be trusted.
Yes. A clean, well-modeled, well-governed data foundation is exactly what AI agents and ML need. We build that foundation and connect it to our AI and agentic operations work.
Yes — we deliver dashboards and self-serve analytics designed for adoption, so people actually use them to make decisions.
Tell us about your data. We reply to every serious enquiry within one business day — and the first consultation is free.
