Enterprise & eCommerce Data Development

Build a modern, reliable data stack that powers revenue, personalisation, and reporting.

  • Data Warehousing

    Redshift, BigQuery, Snowflake

  • ETL/ELT Pipelines

    dbt, Glue, Airflow

  • Master & Product Data (MDM / PIM)
  • BI & Dashboards (Power BI, Looker Studio, Tableau)
  • Commerce Integrations (Magento, Shopify, WooCommerce)
  • Event Tracking & CDP (GA4, server-side, Segment)
  • Privacy & Compliance (GDPR, consent, retention)

We design and implement data platforms that unify product, order, customer and marketing data—so your teams can trust a single version of truth.

From ingestion to activation: batch and streaming pipelines, modelled in the warehouse, exposed via APIs and embedded in the tools your teams use every day.

Typical outcomes: faster reporting, attribution clarity, better merchandising decisions, real-time personalisation, and lower data ops overhead.

At JAS Digital, we specialise in enterprise and commerce data stacks across AWS, Azure and GCP—covering ingestion (APIs, S3, Kafka), transformation (dbt), governance (catalogue, lineage, PII), and activation (BI, reverse ETL, marketing APIs).

We partner with product, marketing and engineering to deliver usable data models, robust SLAs and clear documentation. Post-launch, we offer managed data operations and continuous optimisation.

Ready to modernise your data foundation? Let’s talk.

Commerce-Ready Data Stack

Own your first-party data, unify channels and activate insights across marketing, merchandising and finance.

  • Step 1

    Data Audit & Objectives

    We review sources (commerce, ERP, CRM, marketing, warehouses), define KPIs and gaps, and map governance, security and compliance requirements.

  • Step 2

    Architecture & Specification

    We design the platform: ingestion, storage, modelling (star/schema), orchestration, SLAs, tooling, costs and a delivery plan with clear milestones.

  • Step 3

    Pipelines & Integrations

    We implement connectors and jobs (API/webhook, batch and streaming), establish testing, observability and data quality checks.

  • Step 4

    Models, BI & Activation

    We model facts/dimensions, publish datasets to BI, and push data to marketing, personalisation and operational systems via reverse ETL/APIs.

  • Step 5

    Run & Optimise

    We operate and evolve the stack: cost/performance tuning, data quality, governance, new sources and new use-cases as your needs grow.