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.

Key Responsibilities
1. Hands-On Pipeline Development & Lakehouse Architecture

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 .

2. Performance Tuning & Optimization (Deep Tech)

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) .

3. Governance, Security & Quality

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.

4. Operations, CI/CD & Dev Ops

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.

Requirements
Required Skills &Qualifications
Must-Haves

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.

Preferred / Nice-to-Haves

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.

Performance Indicators(How success is measured)

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.

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