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Selected delivery experience · Client-confidential

Real platform work.
Details protected.

Representative project experience behind Datafor Labs, anonymised to protect client, employer and project confidentiality. The technologies, delivery responsibilities and outcomes remain factual.

05Project contexts
Azure + AWSCloud platforms
Build + migrateDelivery scope

01 / DELIVERY EXPERIENCE

What the work actually involved.

Concrete engineering responsibilities across platform builds, migrations, analytics products and production operations.

01

Regulated enterprise analytics

Manufacturing and laboratory analytics foundation

A cloud analytics platform integrating manufacturing, laboratory and enterprise application data for analytics and data science.

Delivery

  • Built metadata-driven ingestion and incremental loading patterns for daily and weekly data flows.
  • Developed and tuned Spark and PySpark workloads for batch and near real-time processing.
  • Implemented data quality, lineage, governance, monitoring and SQL performance improvements.

Outcome

More reliable production-ready datasets, clearer operational metrics and more efficient data processing.

  • Azure Data Factory
  • Databricks
  • Synapse
  • ADLS Gen2
  • PySpark
  • SQL
  • SAP BW
02

Enterprise platform modernisation

Legacy-to-lakehouse platform migration

A global migration programme designed to centralise enterprise data and standardise ingestion, transformation and analytics delivery.

Delivery

  • Migrated structured and semi-structured datasets into a modern Azure data platform.
  • Built layered dbt models, reusable SQL transformations, tests and reconciliation controls.
  • Introduced incremental models, partitioning, source freshness checks, documentation and lineage.

Outcome

Reliable dataset onboarding with traceable transformations, stronger quality controls and improved discoverability.

  • Azure
  • Databricks
  • dbt Core
  • Delta Lake
  • PySpark
  • Power BI
  • CI/CD
03

Connected asset analytics

Cloud analytics for IoT operations

An AWS-based analytics platform synchronising connected-asset data into operational dashboards, data marts and business metrics.

Delivery

  • Built Airflow pipelines and Spark processing jobs across AWS Glue and Databricks.
  • Implemented serverless calculations, search indexes and integrations with downstream services.
  • Optimised SQL and document-database workloads for accuracy, flexibility and performance.

Outcome

Processed datasets and operational metrics used to analyse sales and inform market-development decisions.

  • AWS Glue
  • S3
  • Athena
  • Lambda
  • OpenSearch
  • Airflow
  • Spark
04

Conversational AI analytics

Analytics layer for a digital service

A data and observability layer for understanding customer interactions, service performance and business KPIs around a conversational product.

Delivery

  • Built ETL workflows, a cloud data warehouse and dashboards for interaction and log analytics.
  • Developed API integrations and optimised Elasticsearch, SQL, MongoDB and Redis workloads.
  • Implemented data cleansing, anomaly handling, transformation validation and Kubernetes delivery workflows.

Outcome

Better visibility into interaction patterns, service savings and opportunities to improve core KPIs.

  • AWS
  • Redshift
  • Kafka
  • Elasticsearch
  • FastAPI
  • MongoDB
  • Kubernetes
05

Retail data and BI

Multi-source retail analytics platform

A centralised analytics platform bringing together sales, inventory and customer-programme data for reporting and decision support.

Delivery

  • Designed the warehouse, dimensional models and end-to-end Azure Data Factory pipelines.
  • Built Power BI datasets and dashboards supported by optimised SQL transformations.
  • Implemented data profiling, missing-value handling, quality checks and database safeguards.

Outcome

Trusted reporting across sales trends, inventory levels, customer behaviour and marketing effectiveness.

  • Azure Data Factory
  • Databricks
  • Azure SQL
  • Power BI
  • Python
  • PySpark
  • MSSQL

02 / START A CONVERSATION

Have a similar platform challenge?

Share the stack, delivery context and outcome you need. We'll respond with a practical next step.

Discuss your requirement hello@dataforlabs.com