Senior Data Engineer

Hire DigITalent Inc.; Toronto; Hybrid; CAD 120,000 - 150,000; Full time

Job summary

Hire DigITalent Inc. is assisting a client in accelerating its digital transformation with a modern data platform on AWS and a Databricks-based architecture. They seek a seasoned Senior Data Engineer to lead architectural decisions, stabilize deliverables, and guide strategy on a high-visibility project.

This six-month contract starts with 3 days in the office in Toronto, Ontario, and offers hands-on leadership, mentorship, and collaboration with data scientists to productionize ML pipelines.

Qualifications

• Proven track record running, building, or enterprise-scaling data platforms in AWS environments.
• Deep expertise in the Databricks ecosystem running natively on AWS (PySpark, Delta Lake, MLflow, Delta Live Tables).
• Practical understanding of dimensional modeling, Medallion architecture, and schema design for analytics.

Responsibilities

• Solve foundational data engineering and Medallion architecture challenges on AWS Databricks, building a robust ingestion framework for diverse, high-volume datasets.
• Apply practical data modeling principles to streamline data layer creation and organize collection/transaction data for analytics.
• Standardize, productionize, and build automated, reusable ML pipelines; partner with Data Scientists to productionize models.
• Cut through complexity and prevent scope creep; establish MVP boundaries and guide task priorities.
• Provide guidance, architectural direction, and hands-on mentorship to internal team members, elevating data engineering standards.
• Use exceptional communication and political acumen to align stakeholders toward cohesive technical decisions.

Skills

AWS data platform

Databricks on AWS

PySpark

Delta Lake

MLflow

Dimensional modeling

Medallion architecture

CI/CD pipelines

Production ML pipelines

Stakeholder alignment

Mentorship

Communication

Job description

Our client is accelerating its digital transformation, running a modern data platform built on AWS and actively moving through a major Databricks implementation. The core objective is transforming data into actionable business products for marketing and promotions. The project team needs a seasoned Senior Data Engineer to help lead the charge, stabilize key deliverables, and guide our technical strategy. This is a critical engagement on a high-visibility, fast-paced project where both strong technical hands-on capability and sharp soft skills are essential.

This is a 6 month contract to start, with 3 days in office.

Key Responsibilities

• Solve foundational data engineering and Medallion architecture (Bronze/Silver/Gold) challenges on AWS Databricks, building a robust ingestion framework for diverse, high-volume datasets.
• Apply practical data modeling principles to streamline data layer creation. Resolve internal debates around model structures and determine optimal ways to structure collection and transactional data for business use.
• Standardize, productionize, and build automated, reusable ML Pipelines. Partner with Data Scientists (who come from non-software engineering backgrounds) to convert standalone models into scalable, production-grade assets.
• Cut through complexity and prevent scope creep ("boiling the ocean"). Evaluate why specific data products are being built, establish clear MVP boundaries, and guide the team on what to tackle first, second, and third.
• Provide guidance, architectural direction, and hands-on mentorship to internal team members, elevating overall data engineering standards.
• Use exceptional communication skills and political acumen to navigate strong, diverse internal opinions, aligning business, technical, and consulting stakeholders toward cohesive technical decisions.

Required Skills & Qualifications

• Proven track record running, building, or enterprise-scaling data platforms in AWS environments.
• Deep expertise in the Databricks ecosystem running natively on AWS (PySpark, Delta Lake, MLflow, Delta Live Tables).
• Practical understanding of dimensional modeling, Medallion architecture, and schema design for analytics. Ability to make pragmatic modeling decisions.
• Demonstrated success creating productionized, reusable machine learning pipelines and assisting Data Scientists with code modularization, CI/CD, and pipeline orchestration.
• Ability to look at data engineering through a product lens evaluating business utility, setting realistic roadmaps, and delivering incremental value.
• Proven capacity to manage conflicting viewpoints, build consensus among strong technical and business voices, and clearly articulate trade-offs.