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OutscalersHuman + AI Engineering

Data Engineering & Cloud

Reliable data and infrastructure, ready for what's next.

We build the pipelines, storage and cloud platforms that turn raw data into trustworthy insight — and give your AI initiatives a solid foundation.

Our approach
Data pipeline on cloud infrastructureData from application databases, SaaS tools and files is ingested, validated and stored in a warehouse within a managed cloud environment, then used for analytics and AI workloads.CLOUD · INFRASTRUCTURE AS CODEApp databaseSaaS toolsFiles & eventsIngestValidatechecksWarehouseAnalyticsdashboardsAI workloadsretrieval · ML

The challenge

Problems we help solve

  • Problem 01

    Data you can't trust

    Reports disagree because data arrives late, duplicated or inconsistent.

  • Problem 02

    Infrastructure that doesn't scale

    Traffic spikes and growing workloads expose fragile, manually managed environments.

  • Problem 03

    AI without a foundation

    Models need clean, governed and accessible data that most organisations don't yet have.

From raw data to decisions

A pipeline you can trust end to end

  1. 01

    Source

    Application databases, SaaS tools, files and events.

  2. 02

    Ingest

    Reliable batch or streaming collection with retries.

  3. 03

    Validate

    Schema, freshness and quality checks before data is trusted.

  4. 04

    Store & model

    Warehouse models designed around business questions.

  5. 05

    Serve

    Dashboards, APIs and AI workloads built on the same data.

Is your data ready for AI?

AI projects succeed or fail on their data foundation. We check these first.

  • Data has a clear owner and definition
  • Access is governed and auditable
  • Pipelines are monitored, with alerts
  • Infrastructure is defined as code
  • Costs are visible and attributed
  • Sensitive data is identified and protected

Deliverables

Data and cloud services

  • Data pipelines

    Batch and streaming ingestion with validation, lineage and monitoring.

  • Analytics foundations

    Modelled warehouses and data marts that support consistent reporting.

  • Cloud architecture

    Scalable, highly available environments designed for your workload and budget.

  • Deployment workflows

    Infrastructure as code, CI/CD pipelines and repeatable environments.

  • AI-ready infrastructure

    Storage, retrieval and compute patterns that support AI and ML workloads.

  • Performance & cost optimisation

    Profiling, right-sizing and caching to improve speed and control spend.

Human + AI approach

Clear roles for people and AI

AI makes our engineers faster. It doesn't replace their judgment — and its output is always reviewed.

Engineers own

  • Design data models, ownership and governance
  • Decide on cloud architecture, security boundaries and cost trade-offs
  • Review every infrastructure change before it is applied
  • Own on-call runbooks and incident response

AI assists

  • Drafts transformation logic and infrastructure templates
  • Generates data-quality checks from schemas
  • Summarises logs and metrics during investigations

AI suggestions are drafts. An engineer reviews, tests and approves before anything is merged.

Technical capabilities

Tools and techniques

Data
PostgreSQLPythonETL / ELTData quality checks
Cloud
AWSContainersServerlessCDN & caching
Operations
Infrastructure as codeCI/CDMonitoringCost managementNetwork management

Delivery process

How an engagement runs

A staged path from first review to a running system, so you see progress early and can adjust at every step.

  1. 1

    Step 01 / 04

    Assess

    Review current data flows, infrastructure and pain points.

  2. 2

    Step 02 / 04

    Design

    Define target architecture, data models and migration path.

  3. 3

    Step 03 / 04

    Build in stages

    Deliver incrementally so value arrives early and risk stays low.

  4. 4

    Step 04 / 04

    Operate

    Monitoring, alerting, documentation and ongoing optimisation.

Related services

Often combined with

$ outscalers start --project your-idea

Ready to build on a stronger data foundation?

Share your goals and constraints. We'll come back with practical options, a realistic plan and the questions worth asking.