Brampton East, Canada | Posted on 08/13/2026
We are looking for a Senior, Super Hands-On DatabricksData Engineer who lives and breathes code, query optimization, and moderndata architecture. In this role, you won't just design architectures onwhiteboards—you will write production PySpark/SQL, optimize Databricksclusters, build streaming and batch pipelines, and enforce data governance.
You will own end-to-end pipeline execution from rawingestion to curated Gold layer models, playing a lead role in modernizing ourLakehouse platform.
Key Responsibilities
1. Hands-On Pipeline Development & LakehouseArchitecture
- Design,build, and maintain enterprise-scale batch and real-time streamingpipelines using PySpark, SQL, Delta Live Tables (DLT), and AutoLoader.
- Implementand refine Medallion Architecture (Bronze Silver Gold) to support downstream BI,reporting, and Machine Learning workloads.
- Enforceschema evolution, ACID transactions, and data compaction using DeltaLake core constructs.
2. Performance Tuning & Optimization (Deep Tech)
- Diagnoseand resolve Spark performance bottlenecks: data skew, OOM errors,excessive shufflings, and memory spills.
- Optimizequeries using Liquid Clustering, Z-Ordering, Data Partitioning, AQE(Adaptive Query Execution), and Photon engine tuning.
- Benchmarkand optimize Databricks compute workloads to minimize DBU (DatabricksUnit) consumption and cloud costs (FinOps).
3. Governance, Security & Quality
- Implementend-to-end data governance, fine-grained access control (row/column-levelsecurity), and lineage tracking using Unity Catalog.
- Automateautomated data quality validation checks and alert mechanisms across thepipeline life cycle.
4. Operations, CI/CD & DevOps
- Automatepipeline orchestration using Databricks Asset Bundles (DABs) or DatabricksWorkflows / Apache Airflow.
- BuildCI/CD pipelines (GitHub Actions, Azure DevOps, or GitLab) for automatedtesting, deployment, and code promotions.
Requirements
Required Skills &Qualifications
Must-Haves
- Experience:8+ years in Data Engineering, with 4+ years of intensive, hands‑onproduction experience on Databricks.
- ProgrammingMastery: Fluent in PySpark, Advanced SQL, and Python.
- DatabricksEcosystem: Deep experience with Delta Lake, Unity Catalog, DeltaLive Tables (DLT), Auto Loader, and Databricks Workflows.
- CloudInfrastructure: Strong hands‑on experience in at least one primarycloud provider (AWS, Azure, or GCP) integration with Databricks(S3/ADLS Gen2, IAM, Key Vaults/Secret Manager).
- DataModeling: Solid understanding of dimensional modeling (Kimball), OneBig Table (OBT) strategies, and data vault patterns.
- CI/CD& Software Engineering: Proficient in Git workflows, unit testingPySpark code (pytest), and deployment automation.
Preferred / Nice-to-Haves
- Certifications: Databricks Certified Data Engineer Professional.
- Streaming: Hands‑on with Apache Kafka, Event Hubs, or Kinesis integration viaStructured Streaming.
- GenAI/ ML Ops: Familiarity with MLflow, Feature Store, or Vector Searchwithin Databricks.
- Infrastructureas Code (IaC): Experience using Terraform to provision Databricksworkspaces and storage resources.
Performance Indicators(How success is measured)
- PipelineReliability: Maintaining strict SLA thresholds on critical Gold-layermodels.
- CostEfficiency: Measurable reduction in DBU costs through effectivecompute profiling and tuning.
- CodeQuality: High test coverage and zero-downtime CI/CD deployments.