Data Engineer

🚀 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, collaborative culture, and high-impact work
• Direct exposure to clients and real business problems — your pipelines will power decisions, not sit in a backlog