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 on whiteboards—you will write production PySpark/SQL, optimize Databricks clusters, build streaming and batch pipelines, and enforce data governance.

You will own end-to-end pipeline execution from raw ingestion 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 streaming pipelines 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.

Enforce schema 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 .

Optimize queries 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 (FinOps) .

3. Governance, Security & Quality

Implement end-to-end data governance, fine-grained access control (row/column-level security), and lineage tracking using Unity Catalog .

Automate automated data quality validation checks and alert mechanisms across the pipeline life cycle.

4. Operations, CI/CD & DevOps





Automate pipeline orchestration using Databricks Asset Bundles (DABs) or Databricks Workflows / Apache Airflow .

Build CI/CD pipelines (GitHub Actions, Azure DevOps, or GitLab) for automated testing, deployment, and code promotions.

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 primary cloud 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 Qualified.

Streaming: Hands-on with Apache Kafka, Event Hubs, or Kinesis integration via Structured Streaming.

GenAI / ML Ops: Familiarity with MLflow, Feature Store, or Vector Search within Databricks.

Infrastructure as Code (IaC): Experience using Terraform to provision Databricks workspaces and storage resources.

Performance Indicators (How success is measured)

Pipeline Reliability: Maintaining strict SLA thresholds on critical Gold-layer models.

Cost Efficiency: Measurable reduction in DBU costs through effective compute profiling and tuning.

Code Quality: High test coverage and zero-downtime CI/CD deployments.

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📌 Senior Databricks Data Engineer (Ontario)
🏢 KData AI
📍 Ontario