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:

Solid 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.

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📌 Senior Databricks Data Engineer (Winnipeg)
🏢 KData
📍 Winnipeg