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 workplace.

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

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📌 Staff Data Engineer (Toronto)
🏢 Loblaw Digital
📍 Toronto