Since 2010
Building Software
Pipelines That Do Not Wake You Up.
Data Engineering Services
We build the data infrastructure everything else depends on: ingestion pipelines, warehouses, and streaming platforms engineered for reliability, with monitoring, documentation, and full source-code ownership.
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 500+
- Engineers
- 3,900+
- Apps Published
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
What data engineering actually delivers
Data engineering is the unglamorous layer that decides whether anything above it works: pipelines that land data on time, warehouses that stay fast as volume grows, and streaming infrastructure that does not lose events under load. When this layer is weak, every dashboard, model, and AI feature built on it inherits the fragility.
We build it with the discipline of production software: tested transformations, monitored pipelines, documented schemas, and capacity plans. For analytics on top of the platform, see our data analytics services.
What our data engineering services cover
Infrastructure we build, run, and hand over.
Batch data pipelines
Scheduled, monitored pipelines from every source system with automatic backfills and schema-change handling.
- ✓Orchestrated DAGs
- ✓Idempotent loads
- ✓Backfill automation
- ✓Failure alerting
Streaming data platforms
Event-driven infrastructure for real-time data, from clickstreams to IoT, with exactly-once processing where it matters.
- ✓Event schema registry
- ✓Stream processing
- ✓Late-arrival handling
- ✓Throughput tuning
Warehouse and lakehouse builds
Scalable storage and query layers with cost governance, partitioning, and lifecycle management designed in.
- ✓Storage layout design
- ✓Partition strategies
- ✓Cost monitoring
- ✓Access controls
Real-time application backends
Data infrastructure powering live features: feeds, leaderboards, and operational dashboards at real traffic.
- ✓Low-latency stores
- ✓Cache architectures
- ✓Read replicas
- ✓Latency budgets
Data platform migration
Legacy warehouses and pipelines moved to modern platforms with cutover plans that keep reporting continuous.
- ✓Migration assessment
- ✓Parallel run validation
- ✓Cutover planning
- ✓Rollback safety
Data quality and governance
Contracts, lineage, and quality gates so consumers can trust what lands and auditors can trace where it came from.
- ✓Data contracts
- ✓Column-level lineage
- ✓Quality test suites
- ✓PII handling policies
What every engagement includes
Every data engineering project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped data engineering roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All data engineering source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with data engineering
Infrastructure our clients run their businesses on.
Enterprise data platform
A warehouse and pipeline estate that serves analytics, ML, and AI features from one governed foundation.
Real-time event backbone
Streaming infrastructure collecting product and operational events with schema governance end to end.
Customer 360 store
An identity-resolved customer store combining behavioural, transactional, and support data.
Live operational dashboards
Real-time data infrastructure behind ops dashboards that reflect what is happening now, not yesterday.
Legacy migration programme
An old warehouse or on-prem estate moved to a modern platform with zero reporting downtime.
How a data engineering Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your data platform goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The data engineering architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. data engineering work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged data platform launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack behind our data platforms
Default choices, stated with reasons.
Orchestration
Workflow orchestration with retries, backfills, and lineage captured by default, so pipeline history is auditable.
Storage and compute
Cloud warehouses and lakehouses with separation of storage and compute, sized and cost-governed to your volume.
Streaming
Durable event logs with schema registries and stream processors, configured for your throughput and ordering requirements.
Reliability
Data quality tests, freshness SLAs, and pipeline monitoring with alert routing, treating data as a production system.
How intelligence flows through the platform
What each layer reads, and what it changes.
Ingestion
Lands source data reliably with schema validation.
Reads
Operational databases, SaaS APIs, events
Changes
Raw landing zones
Processing
Transforms and enriches data with quality gates.
Reads
Raw and staged data, contracts
Changes
Modelled datasets
Serving
Exposes data to analytics, ML, and applications.
Reads
Modelled datasets, access policies
Changes
Serving views and caches
What's included, standard
The baseline on every data engineering engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each data engineering release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Capacity and cost plan
A scaling and cost projection for your platform at current and projected volumes, documented.
Engineered pipelines vs script soup
What changes when data infrastructure is treated as production software.
| Criteria | Consideration | Engineered Platform | Cron Scripts and Spreadsheets |
|---|---|---|---|
| Failure behaviour | Alerts, retries, backfills | Silent breakage, manual fixes | |
| Schema changes | Contracts with validation | Break downstream consumers | |
| Onboarding a data dev | Documented in days | Archaeology for months | |
| Cost visibility | Monitored and governed | Surprise invoices | |
| Audit trail | Lineage and logs | Who knows what ran when |
/11
Why teams pick us for data engineering
The differences live in engineering habits, not pitch decks.
Production software discipline
Pipelines are versioned, tested, and monitored like the software they are, not scripts on a server nobody owns.
Reliability budgets
Freshness SLAs and latency budgets agreed upfront, with monitoring that holds us to them, not just you.
Cost governance built in
Storage partitioning, query optimisation, and cost monitoring designed in from the start, so the bill scales slower than the data.
Handover-ready platforms
Documented schemas, lineage, and runbooks mean your team can run the platform without us, and many do.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with data platforms at serious volume among them.
Data engineering FAQs
$ moxace launch --nowReady for Data Infrastructure That Holds?
Talk to our data engineering team about a fixed-scope platform build with reliability engineering and full ownership.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
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