Senior Consultant, Databricks Data Engineer, Data & AI

KPMGVancouver, BC
$73,000 - $102,000

About The Position

At KPMG, you’ll join a team of diverse and dedicated problem solvers, connected by a common cause: turning insight into opportunity for clients and communities around the world. Are you a talented leader with a proven track record for motivating teams and delivering exceptional client service? 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

  • Certifications: 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.
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