Senior Databricks Data Engineer

Brampton East, Canada | Posted on 08/13/2026

We are looking for a Senior, Super Hands-On Databricks Data Engineer who lives and breathes code, query optimization, and modern data 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 our Lakehouse platform.

• Design,build, and maintain enterprise-scale batch and real-time streamingpipelines using PySpark, SQL, Delta Live Tables (DLT), and Auto Loader
  .
• Implement and refine Medallion Architecture (Bronze
  Silver
  Gold) to support downstream BI,reporting, and Machine Learning workloads.
• Enforceschema evolution, ACID transactions, and data compaction using Delta Lake core constructs
  .

• Diagnose and 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
  .
• Benchmark and optimize Databricks compute workloads to minimize DBU (Databricks Unit) consumption and cloud costs (Fin Ops).

• Implementend-to-end data governance, fine-grained access control (row/column-level security), and lineage tracking using Unity Catalog
  .
• Automateautomated data quality validation checks and alert mechanisms across the pipeline life cycle.

• Automatepipeline orchestration using Databricks Asset Bundles (DABs) or Databricks Workflows / Apache Airflow
  .
• BuildCI/CD pipelines (Git Hub Actions, Azure Dev Ops, or Git Lab) for automated testing, deployment, and code promotions.

• Experience:8+ years in Data Engineering
  , with 4+ years of intensive, hands‑on production experience on Databricks
  .
• Programming Mastery: Fluent in PySpark, Advanced SQL
  , and Python.
• Databricks Ecosystem: Deep experience with Delta Lake, Unity Catalog, Delta Live Tables (DLT), Auto Loader, and Databricks Workflows
  .
• Cloud Infrastructure: 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).
• Data Modeling: Solid understanding of dimensional modeling (Kimball), One Big Table (OBT) strategies, and data vault patterns.
• CI/CD& Software Engineering: Proficient in Git workflows, unit testing
  
  PySpark code (pytest), and deployment automation.

• Certifications: Databricks Certified Data Engineer Professional.
• Streaming: Hands‑on with Apache Kafka, Event Hubs, or Kinesis integration via Structured Streaming.
• GenAI/ ML Ops: Familiarity with MLflow, Feature Store, or Vector Searchwithin Databricks.
• Infrastructure as Code (IaC): Experience using Terraform to provision Databricksworkspaces and storage resources.

• Pipeline Reliability: Maintaining strict SLA thresholds on critical Gold-layermodels.
• Cost Efficiency: Measurable reduction in DBU costs through effectivecompute profiling and tuning.
• Code Quality: High test coverage and zero-downtime CI/CD deployments.