Data Platform Engineering

Data and Platform Engineering

We design and run governed data platforms that extract, cleanse,
and transform data with precision enabling curated, catalogued,
and trusted datasets for analytics, AI agents, and business applications.

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A Scalable, Future-Proof Backbone
for AI and Decisions.

We engineer cloud, on-prem, and hybrid data platforms that unify fragmented systems into governed, performance-driven ecosystems. From ingestion to conformance, every layer is built with security, observability, and compliance in mind. Using domain-ready data models and automation, we deliver clean, catalogued, and AI-ready data that powers analytics, machine learning, and autonomous agents across financial services, retail, education, and beyond.

How it Works

Design

We create a unified data architecture that defines how data is collected, integrated, and governed—laying the foundation for a secure, scalable, and high-performing enterprise data ecosystem.

Build

We build automated data pipelines and workflows that extract, cleanse, and transform information from every source. Using scalable infrastructure and DataOps practices, we turn fragmented data into structured, reusable, and AI-ready assets.

Govern

We embed governance and observability into every layer—enforcing data lineage, quality, and security policies. Automated cataloguing and FinOps tracking ensure every data flow is traceable, compliant, and cost-controlled.

Deliver

We deliver curated, validated, and catalogued data to downstream users through data marts, APIs, and semantic layers. Automated reconciliations and performance tuning ensure trusted, business-ready data that powers analytics, AI, and confident decision-making at scale.

Our Core Capabilities

Build a governed, scalable, and AI-ready data foundation.

Enterprise Data Modelling: Banking Reference Data Model (EDM) and domain-specific extensions.
Data Lake & Warehouse Design: Cloud-native architectures on Azure, Snowflake, Databricks.
Data Quality & Standardization: Automated profiling, cleansing, and reconciliation (iCEDQ, IDMC).
Metadata & Master Data Management: Unified catalogs, golden records, and lineage visibility.
Performance Optimization: Partitioning, caching, and elastic scaling for analytical workloads.

Connect every system, source, and application through intelligent data pipelines.

API-Led Integration: Secure, reusable APIs via Azure API Management / Informatica iPaaS.
Event-Driven Pipelines: Real-time streaming using Kafka / Event Hub for instant data propagation.
Ingestion Frameworks: Batch, CDC, and hybrid ingestion accelerators for structured & unstructured data
Cross-Platform Connectivity: 300+ connectors across core banking, CRM, ERP, and external feeds.
Integration Automation: Infrastructure-as-Code and CI/CD pipelines for rapid deployment.

Enforce trust, compliance, and accountability across your data estate.

Data Lineage & Cataloguing: End-to-end traceability using Microsoft Purview, Informatica CDGC, BigID.
Security & Compliance: Role-based access, encryption at rest/in-transit, GDPR / PDPA / UAE CB compliance.
Data Stewardship Frameworks: Defined ownership and approval workflows for critical data assets.
Policy Enforcement: Automated masking, classification, and retention controls.
Monitoring & Observability: Platform health, usage analytics, and alerting via CloudUnity and Azure Monitor.

Move from legacy to modern cloud platforms faster and safer.

Automated Discovery & Mapping: Source-to-target lineage extraction using BigTapp’s Migration Toolkit.
Hybrid Migration Approach: Top-down & bottom-up strategies for curated and consumption layers.
Cloud Data Platform Migration: On-prem to Azure, Snowflake, or Databricks with zero data loss.
Regression Testing & Validation: Automated reconciliation and report comparisons.
Cost & Performance Optimization: FinOps guardrails for resource utilization and ROI tracking.

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