What we do
Data Engineering Services
A data foundation your team can trust — and operate without us.
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.
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What we build
Data pipelines that don't fail silently. Ingestion and transformation across batch and streaming, with reliability engineered in — idempotency, retries, schema-change handling, and alerting that surfaces a broken pipeline before the business notices bad numbers. Built with the tools that fit your stack (Airflow, dbt, Spark, Kafka, cloud-native services), chosen for a documented reason rather than fashion.
A data platform / lakehouse that scales. A coherent architecture — warehouse, lake, or lakehouse — with clear layers (raw, staged, curated), sensible storage and compute separation, and cost controls so the bill doesn't run away as volume grows. The goal is a platform where adding a new dataset or consumer is a routine step, not a re-architecture.
Data quality and governance as first-class concerns. Automated data-quality checks and tests in the pipeline (not manual spot-checks after the fact), lineage so you can trace any number back to its source, a data catalog people actually use, and access controls and PII handling that satisfy compliance (GDPR, HIPAA, SOC 2) — see DevSecOps Consulting.
Streaming and real-time data where the domain needs it — event-driven pipelines with the ordering and latency guarantees the use case actually requires, and the discipline not to add streaming complexity where a batch job would do.
Analytics and ML enablement. The engineering foundation that makes analytics and ML reliable — feature pipelines, reproducible datasets, and inference data flows that respect governance and PII boundaries — so your data scientists build on solid ground instead of firefighting data issues.
Principles: how we approach data engineering
1.
Governed by default, not after an incident.
Quality, lineage, and access are designed into the platform from the start — retrofitting them after a bad-data or privacy incident is far more expensive.
2.
Match the architecture to the workload.
Not every team needs streaming, a lakehouse, or a warehouse-per-domain. We build what your data volume, latency needs, and team maturity actually justify.
3.
Cost is an architectural constraint.
Cloud data platforms are where budgets quietly run over. We treat FinOps discipline as part of the design, not a later clean-up.
4.
The team has to be able to run it.
A data platform your team can't operate calmly is a liability. Operability and capability transfer are primary constraints, not afterthoughts.
How we work with your teams
Our senior data engineers embed with your squads, using your tools and repos. We map your current data landscape and pain points with the people who live them, design and build the platform and pipelines in the flow of real work (not a sandbox proof of concept), author ADRs for every consequential decision, and pair and coach your engineers so they own and extend the platform. Because we're senior-only, your team sees not just what we build but how we reason about the trade-offs — and the practice sticks. See CoE Design & Transition and Delivery as Training.
Why this matters
CTO / VP Engineering:
A governed, documented data platform your organisation owns — reliable numbers, controlled cost, and no single-vendor dependency for every change.
A governed, documented data platform your organisation owns — reliable numbers, controlled cost, and no single-vendor dependency for every change.
Head of Data / Analytics:
Pipelines that don't fail silently, lineage you can trust, and a platform where new data products are routine.
Pipelines that don't fail silently, lineage you can trust, and a platform where new data products are routine.
Data scientists & analysts:
Reliable, reproducible data to build on, instead of spending half their time debugging pipelines.
Reliable, reproducible data to build on, instead of spending half their time debugging pipelines.
Compliance & Security:
PII handling, access controls, and audit-ready lineage designed in.
PII handling, access controls, and audit-ready lineage designed in.
Finance / FinOps:
platform cost calibrated to actual need, with visibility instead of surprise cloud bills.
platform cost calibrated to actual need, with visibility instead of surprise cloud bills.
When to bring us in
Your dashboards disagree and no one fully trusts the numbers.
Pipelines break silently and data issues surface downstream, late.
Every new analytics or ML request becomes a bespoke, fragile project.
You're standing up a data platform (warehouse, lake, or lakehouse) and want the right architecture from the start.
You're preparing for analytics or ML at scale and need a reliable, governed foundation first.
Cloud data costs are climbing faster than the value you're getting from them.
Related
F. A. Q.
What's the difference between data engineering services and data engineering consulting?
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.
Do we need a data warehouse, a data lake, or a lakehouse?
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.
Can you work with our existing stack?
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.
How do you handle data governance and PII?
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, not bolted on later.
Will we depend on you to run the platform afterwards?
No, by design. The engagement is structured around capability transfer — documentation in your repos, trained internal owners, and a skills matrix — so your team operates the platform without us.
Get an honest read on your data foundation
In a 4-week Blueprint Sprint, we assess your current data landscape, pipelines, and governance, surface the load-bearing gaps, and produce a data-platform roadmap your team can start running immediately.
Start your Blueprint Sprint