Staff Data Engineer in Toronto

What You’ll Do

• Architecture & Technical Leadership
  • Lead system‑level design for scalable, reliable, and high‑performance data platforms supporting batch, streaming, and real‑time use cases.
  • Drive architectural improvements across data pipelines, metadata layers, APIs, and service dependencies.
  • Partner with Product, Backend, and AI teams to translate complex business requirements into robust technical solutions.
• Data Engineering & Platforms
  • Architect, build, and optimize large‑scale data pipelines using PySpark, Dataproc, Airflow, GCS, Parquet, and GCP services.
  • Design and maintain data models that support analytics, reporting, experimentation, and measurement at scale.
  • Implement strong data quality, validation, observability, and monitoring practices to ensure data trust and reliability.
• Backend & API Enablement
  • Contribute to the design and evolution of backend services and APIs that expose measurement, filtering, and reporting capabilities.
  • Reduce unnecessary API and metadata dependencies to unlock better performance and flexibility, including deeper and more effective use of analytics engines such as Druid.
  • Collaborate closely with backend engineers on service design, scalability, and performance tuning.
• AI & Data‑for‑AI Enablement
  • Partner with Data & AI teams to enable AI‑driven features through high‑quality, well‑modelled, and inference‑ready data pipelines.
  • Support Loblaw’s broader AI strategy (including LDIA initiatives) by designing data foundations that power experimentation, automation, and intelligent decision‑making.
  • Help bridge traditional data engineering with emerging AI‑enabled use cases.
• Engineering Excellence & Mentorship
  • Lead design reviews and code reviews, setting standards for performance, readability, testing, and maintainability.
  • Mentor and coach engineers across experience levels, helping raise overall engineering maturity.
  • Drive continuous improvement across pipeline performance, cost efficiency, reliability, and operational excellence.

Does This Sound Like You?

• BA/BS in Computer Science, Engineering, Math, or a related field (advanced degree is a plus).
• Senior‑ or Staff‑level Data Engineer with experience owning production‑critical, large‑scale systems.
• Deep hands‑on expertise with PySpark and distributed data processing, including performance optimization.
• Strong experience with cloud data platforms, preferably GCP (Dataproc, GCS, BigQuery).
• Strong SQL skills with experience querying and optimizing large analytical datasets.
• Experience with non‑relational and analytical data stores (e.g., Druid, Bigtable, Elasticsearch, or similar).
• Solid programming experience in Python, Scala, or Java.
• Experience with orchestration tools such as Airflow and operating production pipelines.
• Strong understanding of data modeling, partitioning strategies, and storage formats (e.g., Parquet).
• Experience working in Agile environments with iterative delivery.
• Strong oral and written communication skills, with the ability to articulate technical concepts to both technical and non‑technical stakeholders.
• Proven team player who thrives in a fast‑paced, collaborative environment.

Nice to Have

• Experience supporting AI/ML workflows or platforms used for model training or inference.
• Experience with real‑time or streaming systems (e.g., Kafka or similar).
• Experience in advertising technology, retail media, or large‑scale measurement systems.
• Experience designing or evolving metadata‑driven systems and APIs.

Legal and Application Notes

• Candidates who are 18 years or older are required to complete a criminal background check. Details will be provided through the application process.
• Requests for accommodation due to a disability (visible or invisible, temporary or permanent) can be made at any stage of application and employment.

Hiring Range $145,000.00 - $195,000.00 per year