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Posted 2 months ago

Principal Data Engineer – Databricks

@ Staffmax

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Toronto, Ontario

Temporary12+ years experience
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SIGNALS

Overview

Staffmax is hiring a Principal Data Engineer on a temporary basis in Toronto, onsite with 4 days in office. The role leads Cloudera-to-Databricks modernization, Lakehouse architecture and finance/risk data platforms for enterprise reporting. The posting states a salary of $150K–$180K and 12–18 years of experience.

Responsibilities:

  • Lead Cloudera-to-Databricks modernization initiatives
  • Design Lakehouse and Medallion Architecture pipelines
  • Refactor Hive/Impala logic into PySpark and Spark SQL
  • Build finance and risk data models and reporting layers
  • Govern data quality, lineage and security controls

Requirements summary:

  • 12–18 years of data engineering experience
  • 8+ years with enterprise Data Warehouse and Data Lake platforms
  • 5+ years of hands-on Databricks and Spark at scale
  • Experience with AWS or Azure and CI/CD tooling
  • Technical leadership and mentoring of senior engineers

Tools & skills

DatabricksClouderaHiveImpalaHBaseAirflowdbtTerraformJenkins
Source and dates

Employer career site / ATS feed · careerpage

Employer posting date
2026-08-28
First seen by our source
2026-10-11
Source record updated
2026-10-11

These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.

Job details

SeniorityLead
DepartmentData & analytics
Experience12+ years
Travelair: none · ground: none
Work settingOffice
PositionMostly sitting
Physical demandsphysical low · computer high
LanguagesEnglish

Summary and details written by AI from the posting; the employer’s posting is the authority.

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Job description

Location: Toronto, ON
Work Arrangement: Onsite / 4 Days in Office
Salary: $150K–$180K
Experience: 12–18 Years

We are looking for a Principal Data Engineer – Databricks to lead enterprise-scale data modernization initiatives, with a strong focus on Cloudera-to-Databricks migration, Lakehouse architecture, finance/risk data, and large-scale data engineering.

Key Responsibilities & Requirements

  • 12–18 years of overall data engineering experience.
  • 8+ years of experience with enterprise Data Warehouse and Data Lake platforms.
  • 5+ years of hands-on experience with Databricks and Spark at scale.
  • Lead modernization of legacy Cloudera platforms (CDH/CDP, Hive, HBase, Impala, Spark) to Databricks Lakehouse.
  • Redesign ingestion, transformation, and consumption patterns from HDFS-based architecture to cloud object storage and Delta Lake.
  • Refactor legacy Hive/Impala logic into PySpark and Spark SQL ELT pipelines.
  • Design and govern enterprise Data Warehouse, Data Lake, and Lakehouse architectures.
  • Implement Medallion Architecture (Bronze, Silver, Gold) and layered data platforms.
  • Develop and optimize large-scale pipelines using PySpark, Spark SQL, Delta Lake, and Databricks SQL.
  • Optimize workloads using Z-ORDER, OPTIMIZE, caching, and cluster configurations.
  • Build semantic and consumption layers for BI, reporting, and analytics.
  • Work with finance and risk data models, including General Ledger, Sub-ledger, financial hierarchies, and credit/liquidity/market risk exposure models.
  • Support reporting capabilities including aggregation, drill-down, and drill-back.
  • Implement data governance, data quality, reconciliation, lineage, metadata, security, and compliance controls.
  • Work with AWS or Azure cloud platforms.
  • Implement CI/CD using Git, Terraform, Jenkins, or Azure DevOps.
  • Use orchestration tools such as Airflow or Databricks Workflows.
  • Experience with dbt is an asset.
  • Act as a technical authority, lead architecture decisions, mentor senior engineers, and establish engineering standards.
  • Partner with finance, risk, analytics, and governance stakeholders to deliver business-ready data solutions.

Nice to Have

  • Experience in BFSI, Capital Markets, or regulatory reporting.
  • Exposure to SAP Finance, Oracle Financials, or S/4HANA.
  • Experience supporting AI/ML workloads.
  • Databricks or cloud certifications.

Impact

  • Lead Cloudera-to-Databricks transformation initiatives.
  • Shape enterprise finance and risk data platforms.
  • Support regulatory, management, and analytical reporting systems.

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