Data Engineer - Retailogists (Granby)

???? Data Engineer (Spark Specialist) Location: Remote Experience Level: Senior (5+ years) Type: Full-time, Permanent About Retailogists Retailogists is a fast-growing startup at the intersection of retail consulting and technology. We combine deep retail domain expertise with technical excellence in big data, full-stack engineering, and AI/ML. Our clients range from fast-scaling digital brands to large, multi-location retailers. We're a nimble team of technologists, consultants, and builders — and we're looking for a Senior Spark Engineer who lives and breathes distributed data processing. If tuning a misbehaving Spark job is your idea of a good afternoon, we want to talk to you. What You'll Do As our Spark specialist, you'll play a leadership role the heavy-lifting layer of our clients' data platform: the pipelines that move, transform, and reshape large volumes of retail data for both internal tools and client-facing products. Most of this runs on AWS Glue today, and you'll be the person we turn to for getting it fast, reliable, and cost-efficient. Responsibilities include: - Designing, building, and maintaining large-scale Spark pipelines on AWS Glue (PySpark and/or Scala) - Tuning Spark jobs for performance and cost — partitioning, shuffles, joins, caching, executor sizing, the works - Debugging and stabilizing production Spark workloads, including spill, skew, and OOM issues - Architecting batch and incremental ETL/ELT patterns across S3-based data lakes (Parquet, Iceberg, Delta, or Hudi) - Integrating Glue with the broader AWS data stack (S3, Athena, Lake Formation, Step Functions, EMR where relevant) - Establishing engineering standards for Spark code — testing, modularity, reusability, and CI/CD for Glue jobs - Partnering with analysts, data scientists, and client teams to land production-ready data where it needs to go What We're Looking For (must-haves) - 5+ years of professional data engineering experience, with a heavy Spark focus - Deep, hands-on Spark expertise: you understand the execution model, the Catalyst optimizer, and how to read a Spark UI to find the real bottleneck - Strong production experience with AWS Glue — Glue jobs, Glue Catalog, crawlers, bookmarks, and the quirks that come with them - Proficiency in PySpark (Scala is a plus) - Comfort working with columnar formats and modern lakehouse table formats (Parquet, Iceberg, Delta, or Hudi) - Solid SQL fundamentals Nice to Have - Experience with cloud data warehouses (Redshift, Snowflake, BigQuery) - Familiarity with dbt and semantic-layer modelling - Exposure to BI tooling (Metabase, Looker Studio, Power BI, etc.) - Background in analytics engineering or BI workflows - Orchestration experience (Airflow, Step Functions, Dagster) - Retail or e-commerce data experience Work Environment - Fully remote with the option to use offices in Montreal / Toronto - Flexible hours, cooperative culture, and high-impact work - Direct exposure to clients and real business problems — your pipelines will power decisions, not sit in a backlog Apply on Kit Job: kitjob.ca/job/2nmqya