Senior Consultant, Databricks Data Engineer, Data & AI

KPMG•Vancouver, BC

152d

About The Position

At KPMG in Canada, our people bring their unique perspectives to Canada’s most important challenges. Here, you can build momentum that reaches beyond our business, develop skills for the future, and take ownership of your career with support at every stage. Join a firm where your career can make a difference. Our team is looking for a Data Engineer with extensive hands-on expertise in Databricks and strong consulting capability. This role will support and lead modernization initiatives from legacy/on-prem data platforms to scalable, secure, and cost-optimized Lakehouse architectures using Databricks and similar technologies.

Requirements

University degree in computer engineering, mathematics, data science or related disciplines
4+ years of professional experience in a related field like Data Engineering, Business Intelligence, or related field with a track record of manipulating, processing, and extracting value from large datasets.
2+ years of hands-on experience with Databricks, including advanced features (Delta Lake, Unity Catalog) with Databricks or cloud certifications with 1-2 years of experience leading workstreams / client-facing delivery.
Strong proficiency in SQL and solid understanding of modern data modeling principles, dimensional modeling, and data warehousing concepts.
Proficiency in Python (or similar scripting languages) for data processing, automation, and analytical workflows
Strong experience working in teams to perform ETL (extract, transform and load) of data from a variety of databases from SQL, NoSQL, etc.
Proven experience leading large-scale data migrations (ETL, workloads, cloud platforms), including migration of legacy data platforms or ETL workloads to cloud-native environments.
Experience applying CI/CD practices to data engineering workflows, including version control, automated deployment, and pipeline orchestration.
Independent ability to review the data quality and data definitions and perform data cleansing and data management tasks.
Experience collaborating within cross-functional and multi-disciplinary teams to solve complex data challenges, including processing semi-structured and unstructured data
Experience in at least one major cloud service: AWS, Azure and GCP with understanding of cloud-native services, identity management, and scalable architecture principles.

Nice To Haves

Databricks Certified Data Engineer (Associate or Professional) and/or relevant cloud certifications (e.g., Azure, AWS, or GCP architecture or data engineering credentials) are preferred.

Responsibilities

Partner with clients to understand business goals, gather requirements, and translate them into actionable technical designs and delivery plans.
Work with the engagement team to translate business and analytics requirements into a data strategy for the engagement including ETL/ELT, data model, and staging data for analysis.
Contribute to end-to-end solution architecture for repeatable, cost-optimized implementations (including non-functional requirements and operational readiness).
Lead delivery of modern data platforms on Databricks (ETL/ELT pipelines, workload migrations, governance enablement).
Implement Delta Lake / Lakehouse patterns including medallion architecture, CDC, incremental processing, and data quality controls.
Develop data pipelines to support streaming, incremental, batch data, etc.
Design and implement scalable batch and streaming pipelines using Spark and modern orchestration patterns.
Apply CI/CD and engineering best practices (version control, automated deployment, testing, and release management) to data engineering workflows.
Establish and operationalize governance using Unity Catalog, including access controls, lineage, and security frameworks.
Support testing and production releases, including troubleshooting, performance tuning, and stabilization.
Proactively contributes to the creation of presentation materials relating to data activities for stakeholder discussions.