Senior Data Engineer AI Personalization & Real-Time Customer Data

Ontario - Canada

Monthly Salary: Not Disclosed

Posted on: 8 hours ago

Vacancies: 1 Vacancy

Job Summary

Join our team and what well accomplish together

The AI Products and Platform (AIPP) team builds the foundational capabilities that enable AI-driven personalization decisioning and real-time customer experiences at scale. Our work supports intelligent interactions across digital and assisted channels helping ensure customers receive timely relevant and responsible experiences.

The team develops and operates platforms for customer segmentation anomaly detection and AI-based decisioning where machine learning models are used to arbitrate recommendations and outcomes. These capabilities rely on high-quality real-time data to function reliably in production.

While Data Engineers on the team do not build machine learning models they play a critical role in enabling and operationalizing AI by designing event-driven data pipelines data architecture and foundational datasets that allow models to be trained scored monitored and safely activated in real time. Their work ensures AI outputs are reliable explainable and actionable across downstream systems.

This is a hands-on engineering role focused on event-driven pipelines customer segmentation and data ingestion.

You will partner closely with Data Scientists Product Managers IVR/CCAI teams and Software Engineers to productionize ML models enable real-time triggers and support internal tooling (such as future offer-loading or segmentation UIs).

What you will do:

Build and operate real-time data pipelines that ingest customer events (payments app behavior IVR interactions) and power ML product decisions across channels.
Develop event-driven architectures using GCP services (Pub/Sub Dataflow Cloud Run BigQuery) to support low-latency triggers and segmentation.
Partner with Data Scientists and ML engineers to productionize AIPP models (e.g. ML2) transforming model outputs into reliable explainable data feeds.
Translate business and marketing requirements into scalable data logic including customer segments eligibility rules and offer mappings.
Enable IVR and CCAI experiences by supporting data flows for intelligent routing proactive messaging and self-serve journeys.
Support internal tools and future UI capabilities by providing clean datasets schemas and APIs for Software and Full Stack Engineers.
Monitor troubleshoot and improve production pipelines ensuring data quality reliability and observability during peak periods.
Optimize for performance and cost balancing streaming workloads BigQuery usage and API integrations.
Enable responsible AI practices through validation lineage and monitoring so personalization decisions remain transparent and trustworthy.
Contribute to long-term architecture and standards helping AIPP evolve into a steady-state personalization platform.

What you bring

4 years of experience in Data Engineering or backend data systems
Strong experience with Python and SQL in production environments
Hands-on experience with GCP especially BigQuery and Pub/Sub
Experience designing and operating streaming or event-driven pipelines
Understanding of data modeling data quality and pipeline reliability
Experience integrating data via APIs including retries rate limiting and failure handling
Bachelors degree in Computer Science Engineering Data Systems or a related technical field OR equivalent hands-on industry experience building production-grade data pipelines

Nice to Have:

Experience with Adobe Experience Platform or other CDPs
Exposure to AI/ML feature pipelines or real-time model scoring
Experience with Dataflow (Apache Beam) dbt or Airflow/Composer
Familiarity with personalization or decisioning platforms (PEGA-like concepts)
Telecom or high-volume consumer data experience

Required Experience:

Senior IC

Join our team and what well accomplish together The AI Products and Platform (AIPP) team builds the foundational capabilities that enable AI-driven personalization decisioning and real-time customer experiences at scale. Our work supports intelligent interactions across digital and assisted channels...

Join our team and what well accomplish together

This is a hands-on engineering role focused on event-driven pipelines customer segmentation and data ingestion.

What you will do:

Build and operate real-time data pipelines that ingest customer events (payments app behavior IVR interactions) and power ML product decisions across channels.
Develop event-driven architectures using GCP services (Pub/Sub Dataflow Cloud Run BigQuery) to support low-latency triggers and segmentation.
Partner with Data Scientists and ML engineers to productionize AIPP models (e.g. ML2) transforming model outputs into reliable explainable data feeds.
Translate business and marketing requirements into scalable data logic including customer segments eligibility rules and offer mappings.
Enable IVR and CCAI experiences by supporting data flows for intelligent routing proactive messaging and self-serve journeys.
Support internal tools and future UI capabilities by providing clean datasets schemas and APIs for Software and Full Stack Engineers.
Monitor troubleshoot and improve production pipelines ensuring data quality reliability and observability during peak periods.
Optimize for performance and cost balancing streaming workloads BigQuery usage and API integrations.
Enable responsible AI practices through validation lineage and monitoring so personalization decisions remain transparent and trustworthy.
Contribute to long-term architecture and standards helping AIPP evolve into a steady-state personalization platform.

What you bring

4 years of experience in Data Engineering or backend data systems
Strong experience with Python and SQL in production environments
Hands-on experience with GCP especially BigQuery and Pub/Sub
Experience designing and operating streaming or event-driven pipelines
Understanding of data modeling data quality and pipeline reliability
Experience integrating data via APIs including retries rate limiting and failure handling
Bachelors degree in Computer Science Engineering Data Systems or a related technical field OR equivalent hands-on industry experience building production-grade data pipelines

Nice to Have: