Senior Data Engineer

Job summary

Design and deliver scalable client data platforms from initial scoping and architecture through production, including high-volume batch and streaming pipelines for AI and machine learning workloads. Advise client stakeholders on technical roadmaps and architecture trade-offs, while ensuring data quality, reliability, monitoring, and effective data modeling.

Job details

About the Company This is a global consultancy that builds machine learning and data engineering solutions for large organizations. Engagements run across many industries, mostly in North America, and usually mean designing data systems from scratch rather than maintaining someone else's. The work is client-facing and covers a wide range of technologies. Each engagement brings a different stack, a different set of stakeholders and a different problem to solve. The Role As a Senior Data Engineer, you'll own data platform design and delivery for client engagements, from scoping and architecture through to production. You'll work directly with client stakeholders and act as their technical advisor as well as the engineer building the system. You'll do well here if you have strong distributed systems fundamentals, can learn a new client environment quickly, and are comfortable explaining architecture decisions to people who aren't engineers. This is a hybrid role based in Montreal or Toronto, with one day a week in the office. Montreal is preferred. What You Will Do Turn client business goals into technical roadmaps, and walk non-technical stakeholders through the trade-offs behind each architecture choice Design scalable data architectures that cover ingestion, storage and processing, built around each client's needs and cloud platform Build data platforms mainly on Microsoft Fabric, adjust to other client stacks when an engagement needs it, and keep pipelines automated, monitored and reliable Build batch and streaming pipelines at high volume that feed AI and ML workloads in production Model data with Star, Snowflake or Data Vault schemas, enforce data quality and validation, and work with Data Scientists on feature engineering and low-latency data access What You Bring 7+ years in data engineering, including client-facing consulting or work at high-growth companies across several projects, industries or tech stacks Strong fundamentals in distributed systems, consistency models, the CAP theorem and large-scale data processing, backed by a CS degree or equivalent experience Expert-level experience with Microsoft Fabric, Databricks, Snowflake or a similar platform, plus orchestration tools such as Azure Data Factory, Airflow, Dagster or Prefect Hands-on experience with lakehouse design using medallion patterns, Spark or Flink, and streaming systems such as Kafka, Event Hubs or Spark Streaming Advanced Python and SQL; experience with PostgreSQL, SQL Server, CosmosDB and MongoDB Fluent French and English (required), with the communication skills to manage client expectations on tight timelines and contribute to scoping and pre-sales Why This Role Your work here is architecture and client advisory, not ticket-driven pipeline maintenance. You'll design platforms from the ground up across several industries and tech stacks, and you'll help shape engagements from the pre-sales stage. Your platforms will support production AI and analytics. With only one office day a week, you get both architectural ownership and client exposure while keeping most of your week remote.

What you’ll do

Design and deliver scalable client data platforms from initial scoping and architecture through production, including high-volume batch and streaming pipelines for AI and machine learning workloads. Advise client stakeholders on technical roadmaps and architecture trade-offs, while ensuring data quality, reliability, monitoring, and effective data modeling.

Requirements

Requires 7+ years of data engineering experience, strong distributed systems and large-scale processing fundamentals, and expert experience with a modern data platform and orchestration tools. Candidates should have hands-on lakehouse, Spark or Flink, streaming, Python, SQL, and database experience, plus fluency in French and English; a computer science degree or equivalent experience is expected.

Listed skills

• SQL · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Data Engineering
• Distributed Systems
• Microsoft Fabric
• Databricks
• Snowflake
• Azure Data Factory
• Apache Airflow
• Dagster
• Prefect
• Apache Spark
• Apache Flink
• Apache Kafka
• Python
• SQL
• Data Architecture
• French And English Communication

Job areas

• Data & Analytics
• Technology
• Software
• Consulting

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