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Data Services

One partner for the whole data lifecycle

From the first source system extract to the dashboard your CEO opens on a Monday morning — designed, built, secured and operated by teams in your region.

Warehousing Engineering ETL / ELT Analytics & BI Big Data API Integration AI & GenAI Managed Ops
Service 01

Cloud Data Warehousing

A data warehouse is only as good as the modelling decisions underneath it. We design layered architectures — raw landing, conformed integration, and curated presentation marts — so that new sources plug in without rewriting everything downstream, and so business users get tables that mean what their names say.

We build on Snowflake, Google BigQuery, Databricks, Amazon Redshift, Azure Synapse and Microsoft Fabric. Where you already have a platform commitment, we work within it. Where you do not, we run a short, evidence-based selection exercise using your actual query patterns rather than vendor benchmarks.

  • Dimensional, Data Vault or wide-table modelling as the workload demands
  • Environment strategy: dev, test and production with CI/CD promotion
  • Workload isolation and cost guardrails so one bad query cannot blow the budget
  • Historical loading and slowly-changing-dimension handling done properly
  • Documentation and data dictionary generated from the code, never stale
Presentation marts business
↑
Conformed integration modelled
↑
Raw landing zone immutable
Service 02

Data Engineering

The difference between a demo and a data platform is engineering discipline. Every pipeline we ship is in version control, has automated tests on the data it produces, emits metrics, alerts a named owner when it fails, and can be redeployed from scratch by someone who has never seen it before.

Our teams work in Python, SQL and Scala across dbt, Apache Airflow, Apache Spark, Kafka, Fivetran and cloud-native services. Infrastructure is defined in Terraform so environments are reproducible and auditable.

  • Idempotent, restartable pipelines with backfill support
  • Data quality tests at ingestion and after transformation
  • Lineage capture from source column to dashboard field
  • Incremental processing to keep compute spend proportional to change volume
  • On-call runbooks written for your team, not for us
Git · reviewed & versioned ci
Tests · 1,240 assertions pass
Observability · alerting live
Terraform · reproducible iac
Service 03

ETL & ELT Development

Extracting data is rarely the hard part. The hard part is doing it repeatedly, without silently dropping records, while source systems change underneath you. We build ingestion frameworks rather than one-off jobs, so onboarding the fortieth source costs a fraction of what the first one did.

We work with commercial tools where they earn their licence — Informatica, Talend, SSIS, IBM DataStage, Oracle Data Integrator, Fivetran, Qlik Replicate — and with open-source stacks where they do not. Change data capture, batch extracts, file drops, SFTP feeds, REST and SOAP APIs, and streaming sources are all in scope.

  • Reconciliation controls that prove row counts and financial totals match source
  • Schema-drift detection so upstream changes fail loudly, not silently
  • Late-arriving and out-of-order data handled by design
  • Reprocessing and audit trails for regulator-facing datasets
Core banking · CDC sync
SAP ECC · batch extract 02:00
Partner APIs · REST 15 min
SFTP file feeds daily
Service 04

Analytics & Business Intelligence

Most organisations do not have a dashboard shortage. They have a trust problem: three teams produce three different revenue numbers and nobody knows which is right. We fix that with a governed semantic layer where every metric has one definition, one owner and a visible calculation.

On top of that we build reporting in Power BI, Qlik Sense, Tableau or Looker — designed around the decisions people actually make, not around every field that happens to exist in the warehouse.

  • Metric catalogue with owners, definitions and certified status
  • Row-level security so each user sees exactly their scope
  • Executive, operational and self-service tiers with different design rules
  • Bilingual English/Arabic reporting where required
  • Adoption tracking so unused reports get retired instead of accumulating
Executive scorecard certified
Sales & margin trend certified
Operations monitor near real time
Regulatory pack audited
Service 05

Big Data & Streaming

When volume, velocity or variety break a conventional warehouse, we design lakehouse and streaming architectures instead: object storage with open table formats such as Delta Lake, Apache Iceberg or Hudi, processed with Spark and fed by Kafka or cloud-native event services.

This is the right pattern for telecom CDRs, IoT and sensor telemetry, clickstream, transaction fraud signals and log analytics — anywhere the data arrives faster than a nightly batch can absorb it.

  • Exactly-once and at-least-once semantics chosen deliberately per use case
  • Tiered storage so cold history stays cheap and hot data stays fast
  • Schema registry and contract enforcement between producers and consumers
  • Backpressure handling and replay for incident recovery
Kafka ingest 3.2B/day
↓
Spark structured streaming running
↓
Delta lakehouse bronze/silver/gold
Service 06

API & System Integration

A warehouse that only feeds dashboards is doing half a job. We push governed data back out into the systems where work happens — CRM, ERP, marketing automation, customer portals and partner platforms — through purpose-built APIs and reverse-ETL patterns.

Integrations are built with authentication, rate limiting, retry semantics, idempotency keys and monitoring from day one, so they survive contact with third-party systems that go down without warning.

  • REST, GraphQL, SOAP and webhook integration patterns
  • OAuth2, mTLS and API-key authentication with secret rotation
  • Reverse ETL to Salesforce, Dynamics, HubSpot and operational systems
  • Contract testing so consumer breakage is caught before deployment
CRM sync 200 OK
ERP writeback 200 OK
Partner webhook retry 0
Service 07

AI, Machine Learning & Generative AI

We take a deliberately unglamorous position on AI: it works when the data underneath it is clean, governed and available. Most organisations asking for AI need a data foundation first. Once that exists, the models are the easy part.

Where the foundation is ready we build forecasting, churn and propensity models, anomaly and fraud detection, document extraction, and retrieval-augmented assistants over your own governed corpus — with evaluation harnesses so you can measure whether the thing is actually working.

  • Feature stores built on the same governed data as your reporting
  • RAG assistants scoped to internal documents with source citation
  • Model monitoring for drift, bias and degradation over time
  • Clear guidance on which use cases are not worth the spend
Feature store 312 features
Churn model auc .87
RAG assistant grounded
Service 08

Managed Data Operations

Building a platform is a project. Keeping it healthy is a discipline. Our managed service takes on day-two responsibility — monitoring, incident response, change delivery, cost optimisation and quarterly architecture review — under a contractual SLA with named engineers rather than an anonymous ticket queue.

Coverage windows follow your region. Gulf, Pakistan and APAC teams overlap enough to provide extended hours or genuine follow-the-sun cover for critical platforms.

  • Defined SLAs for pipeline freshness, incident response and resolution
  • Monthly cost review with concrete optimisation recommendations
  • Change backlog delivered in fixed capacity blocks
  • Transparent reporting — you see the same dashboards we do
Freshness SLA on target
Open incidents 0 P1 · 2 P3
Monthly spend -12% MoM

Not sure which of these you need?

Most clients start with a short assessment. We look at your sources, your reporting pain and your budget, then tell you where the leverage actually is.

Request an assessment See packaged solutions