Data Engineering Services
MetaProject provides data engineering services for teams whose data has outgrown its plumbing. The dashboards disagree, the pipelines break silently, "one source of truth" is three sources that don't reconcile, and every new analytics or ML request turns into a bespoke project.
The problem is rarely a missing tool. It's the absence of a coherent, governed data platform and the practice to run it. We build that foundation embedded with your team, senior-only, and leave the capability in your hands rather than becoming the only people who understand it.
What we build
Principles: how we approach data engineering
How we work with your teams
Why this matters
A governed, documented data platform your organisation owns: reliable numbers, controlled cost, and no single-vendor dependency for every change.
Pipelines that don't fail silently, lineage you can trust, and a platform where new data products are routine.
Reliable, reproducible data to build on, instead of spending half their time debugging pipelines.
PII handling, access controls, and audit-ready lineage designed in.
platform cost calibrated to actual need, with visibility instead of surprise cloud bills.
When to bring us in
Data engineering cost
“Their level of commitment and dedication to the success of the overall effort is their most impressive attribute.”
“Everything was delivered within the timelines given and with high standards.”
Data engineering FAQ
A data audit costs $10k–$25k. Building a data platform with pipelines, modelling and governance typically costs $60k–$200k over 2–6 months. An embedded senior data team costs $20k–$40k a month.
Consulting is the assessment and design: what platform and pipelines you need and why. Services is the build. We do both, embedded: we design the data platform and build it with your team, ending with your engineers able to run and extend it.
It depends on your workloads. Warehouses suit structured analytics; lakes suit large, varied, raw data; lakehouses aim to combine both. We recommend based on your data volume, latency needs, query patterns, and team maturity, not on a default preference.
Yes. We work with your existing warehouse and orchestration (Snowflake, BigQuery, Databricks, Redshift, Airflow, dbt, Kafka, etc.) rather than imposing a stack. Tooling follows from your architecture and constraints.
As first-class design concerns: access controls, PII classification and handling, lineage, and audit-ready evidence mapped to the frameworks your business needs (GDPR, HIPAA, SOC 2), built into the platform from the start.
No, by design. The engagement is structured around capability transfer: documentation in your repos, trained internal owners, and a skills matrix. Your team operates the platform without us.
Tell us what you are building. On a 30-minute call a senior engineer walks through your data platform and pipelines. Or get a first view of the team and timeline in two minutes.