Senior Databricks Engineer
KData Inc.
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Downtown Toronto (Richmond / Adelaide / King), Canada | Posted on 11/02/2026
- City Downtown Toronto (Richmond / Adelaide / King)
- Country Canada
Job Description
Role Overview
We are seeking an experienced Senior Databricks Engineer with a deep background in distributed data systems to architect, scale, and optimize enterprise-grade lakehouse solutions. In this role, you will take technical ownership of end-to-end batch and streaming pipelines, implement robust governance and security controls via Unity Catalog, drive platform-wide performance tuning and FinOps initiatives, and mentor engineering talent across the data organization.
Key Responsibilities
Lakehouse Architecture & Modeling: Spearhead the end-to-end technical design and implementation of highly scalable lakehouse pipelines adhering to the Medallion Architecture (Bronze, Silver, Gold) , dimensional modeling principles, and robust Change Data Capture (CDC) patterns.
Pipeline Development & Orchestration: Build high-throughput, fault-tolerant batch and real-time streaming ingestion pipelines using PySpark, Spark SQL, Delta Lake, Auto Loader, and Databricks Workflows / Lakeflow Jobs .
Performance Tuning & FinOps Leadership: Profile workloads, analyze execution plans, and optimize compute configurations to drive down runtimes and minimize Databricks Unit (DBU) consumption using liquid clustering, Photon, serverless compute, and automated table maintenance (OPTIMIZE, VACUUM).
Enterprise Governance & Security: Architect and enforce fine-grained access control policies (row/column-level security, data masking), data lineage tracking, and auditing using Databricks Unity Catalog across multi-workspace and multi-region environments.
DevOps, IaC & CI/CD: Establish automated deployment pipelines, testing frameworks, and code promotion standards using Databricks Asset Bundles (DABs) , Terraform, and CI/CD tools (GitHub Actions, Azure DevOps, or GitLab CI).
Technical Mentorship & Cross-Functional Alignment: Serve as the subject matter expert on the Databricks ecosystem, establishing code quality benchmarks, leading architecture reviews, and mentoring mid-to-senior engineers while partnering closely with Analytics and Machine Learning teams.
Requirements
Required Qualifications
Experience: 8+ years of hands-on software and data engineering experience, with at least 5+ years of dedicated, deep technical experience architecting and building production-grade solutions on the Databricks platform .
Core Languages: Advanced, expert-level proficiency in Python (PySpark, Spark Core) and Advanced SQL (complex analytical queries, window functions, and physical query execution plan tuning).
Lakehouse Internals: Deep operational mastery of Delta Lake internals (ACID guarantees, transaction logs, data skipping, Z-ordering/liquid clustering, compaction, and schema evolution) along with Structured Streaming.
Cloud Architecture & Networking: Substantial production experience across cloud storage and networking architectures ( AWS, Azure, or GCP ), including private link, VPC peering/vNet injection, IAM roles, and secure storage access patterns (e. g., ADLS Gen2, AWS S3).
Governance & Access Management: Proven track record designing and managing Databricks Unity Catalog at scale (catalogs, schemas, volumes, external locations, privilege inheritance, and metastore integrations).
Software Engineering Rigor: Strong background in enterprise-level version control workflows (Git), automated unit/integration testing (TDD), modular software design, and automated CI/CD practices.
Preferred Qualifications
Certifications: Databricks Certified Data Engineer Professional or Databricks Certified Machine Learning Professional.
Infrastructure as Code (IaC): Production experience provisioning workspaces, clusters, and access permissions using Terraform .
Modern Data Stack: Practical experience with dbt (dbt-databricks), Apache Kafka, Azure Event Hubs, Delta Live Tables (DLT), or MLflow integration.
BI & Consumption Layer: Hands-on experience optimizing Databricks Serverless SQL Warehouses for downstream consumption tools such as Power BI, Tableau, or Looker.
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