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. Robust experience with cloud data platforms, preferably GCP (Dataproc, GCS, BigQuery). Robust 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 #J-18808-Ljbffr

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