Data analytics mel fast q9

Rapid analytics, dashboards and machine learning for Melbourne businesses — from ingestion to insights in weeks.

Data pipeline

Overview

Dealparallax delivers fast, production-grade analytics in Melbourne and across Australia: ETL, data warehouses, BI dashboards, and small-scale ML models tailored to enterprise and SMB needs.

  • Rapid discovery & scoping
  • Automated ETL / ELT pipelines
  • Interactive dashboards and reporting
  • ML prototypes and MLOps readiness

Common use cases

Melbourne-focused solutions
Retail analytics

Retail & eCommerce

Sales attribution, cohort analysis, demand forecasting.

Fintech analytics

Financial analytics

Risk indicators, anomaly detection, revenue dashboards.

Operations analytics

Operations & IoT

Telemetry ingestion, monitoring, predictive maintenance.

Performance & outcomes — sample metrics

ProjectTime to MVPData sourcesKey impact
Retail dashboard (Melbourne)4 weeksPOS, CRM, Web+18% conversion
IoT monitoring6 weeksEdge sensors, Cloud30% downtime reduction
Fintech insights8 weeksTransactions, LogsFaster fraud detection
Lead data scientist

Meet the team — Lead Data Scientist

Our Melbourne-based lead brings enterprise analytics experience, production ML deployments, and a focus on explainable results for stakeholders.

  • 10+ years in analytics, BI and ML
  • Expertise in Snowflake, BigQuery, dbt, Airflow
  • Focus on ethical and reliable models

Our process

Rapid scoping, lightweight data assessment, proof-of-concept dashboards or models within 2-4 weeks.

Robust ETL pipelines, warehousing, CI/CD for analytics and model deployment with monitoring.

Scale pipelines, tune models, roll out dashboards and handover runbooks and knowledge to your team.

Tech & integrations

We integrate with common Australian stacks and cloud providers: Snowflake, BigQuery, AWS, GCP, Azure, dbt, Looker, Power BI, Tableau.

ETLData WarehouseBIMLOps

Get quick ROI

We prioritise high-impact KPIs and deliver dashboards and alerts that let your team act within days of deployment.