Postuler

Publié il y a 6 jours

Lead Data Engineer / Data Product Lead – Snowflake

@ Global Technical Talent, an Inc. 5000 Company

Toronto, Ontario

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Postuler
SIGNAUX

Résumé des exigences :

  • This is a hands-on technical leadership role requiring deep expertise in Databricks, PySpark, Spark SQL, Delta Lake , cloud data platforms, and modern data engineering, along with the…
  • Partner with Product Owners, architects, business stakeholders, analytics teams, and engineering leads to translate requirements into scalable technical solutions.
  • Design reusable and modular data models that support evolving reporting and analytical requirements.
  • Required Skills
  • Strong data engineering experience with expertise in ETL/ELT, data modeling, lakehouse architecture , and enterprise-scale data products.
  • Deep hands-on experience with Azure Databricks or Databricks on a major cloud platform.
Source et dates

Type de source non vérifié · apify-linkedin

Date publiée par l’employeur
2026-11-04
Première détection par notre source
2026-11-10
Mise à jour du dossier source
2026-11-10

Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.

Global Technical Talent, an Inc. 5000 Company

ChampValeur
Offres sur Jobsy8
Description

Lead Data Engineer / Data Product Lead – Snowflake

Location: Toronto, ON


Onsite Flexibility: Hybrid


Contract Details
  • Position Type: Contract
  • Contract Duration: 6 months
  • Pay Rate: C$60.00–C$80.00 / Hour (CAD)

Job Summary

Our client is seeking an experienced Sr. Data Engineer / Data Product to support the design and delivery of enterprise-scale data products within a complex life sciences environment. This is a hands-on technical leadership role requiring deep expertise in Databricks, PySpark, Spark SQL, Delta Lake, cloud data platforms, and modern data engineering, along with the ability to own data products end to end from architecture and design through development, deployment, production support, and downstream enablement.


Key Responsibilities
  • Own one or more enterprise data products across architecture, design, development, testing, deployment, and ongoing enhancement.
  • Design and build scalable data solutions using Databricks, PySpark, Spark SQL, Delta Lake, and Python.
  • Contribute to architecture across data ingestion, transformation, medallion/lakehouse layers, semantic models, data quality, lineage, and downstream consumption.
  • Partner with Product Owners, architects, business stakeholders, analytics teams, and engineering leads to translate requirements into scalable technical solutions.
  • Design reusable and modular data models that support evolving reporting and analytical requirements.
  • Technical design and implementation reviews to ensure adherence to enterprise architecture and engineering standards.
  • Coordinate delivery across onshore/offshore engineering teams and third-party vendors.
  • Manage technical dependencies, priorities, risks, and delivery across multiple concurrent workstreams.
  • Establish standards around data quality, observability, lineage, validation, testing, deployment, and production support.
  • Mentor engineers and establish clear ownership across functional areas.
  • Explore emerging Databricks capabilities including Mosaic AI, MLflow, Genie, semantic models, and AI-enabled analytics.

Required Skills
  • Strong data engineering experience with expertise in ETL/ELT, data modeling, lakehouse architecture, and enterprise-scale data products.
  • Deep hands-on experience with Azure Databricks or Databricks on a major cloud platform.
  • Advanced experience with PySpark and Spark SQL.
  • Strong experience with Delta Lake and Delta tables, including scalable transformation and historical-processing patterns.
  • Experience implementing medallion architecture across raw, curated, and business-ready data layers.
  • Hands-on experience with Databricks capabilities such as:

- Unity Catalog - Auto Loader - Databricks SQL - Jobs and Workflows - Notebooks and Repos - Change Data Feed - Structured Streaming


  • Experience integrating Databricks with cloud storage and orchestration platforms such as ADLS Gen2, Azure Data Factory, AWS S3, or similar services.
  • Strong understanding of Spark performance tuning, optimization, partitioning, and cost management.
  • Experience with CI/CD, Infrastructure as Code, source control, and production deployment patterns.
  • Proven ability to own complex data products through the full delivery lifecycle.
  • Strong stakeholder-management and communication skills.
  • Experience leading and mentoring distributed engineering teams.

Preferred Skills
  • Experience within pharmaceutical, biotechnology, or life sciences environments.
  • Knowledge of commercial, patient, payer, market access, CRM, or channel-partner data.
  • Experience with specialty pharmacy / specialty distributor data, 3PL data, Veeva CRM, Salesforce, marketing data, or patient/access datasets.
  • Experience with advanced Unity Catalog governance, lineage, role-based access, row-level security, and column masking.
  • Exposure to Mosaic AI, MLflow, Genie, GenAI, semantic models, or agent-based analytical solutions.
  • Databricks certification or equivalent demonstrated expertise.

About GTT

GTT is an NMSDC-certified Minority Business Enterprise (MBE) headquartered in Portsmouth, New Hampshire. GTT is a subsidiary of Chenega Corporation, an Alaska-based company and one of the largest Alaska Native-owned corporations. An Inc. 5000 honoree and one of SIA's Best Staffing Firms to Work For, GTT connects professionals with Fortune 500 organizations across IT, engineering, construction, utilities, life sciences, financial services, and healthcare throughout the U.S. and Canada. We're committed to diverse, inclusive workplaces and to placing people in roles where they can do their best work.


Job Number: 26-18394 Industry: Data & Analytics


#gttca

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