Senior Data Science Developer

Cynet Systems Inc; Toronto; On-site; CAD 80,000 - 100,000; Full time

Job summary

A leading company is seeking a skilled data engineer to create and maintain data storage solutions and automate data pipelines. The role involves enhancing analytics environments and conducting knowledge transfer sessions. Ideal candidates will have extensive experience with Azure technologies and data analytics tools.

Qualifications

• Experience with Azure Storage and Data Lake in real world implementations.
• Experience automating data pipelines using Microsoft Azure technologies.
• Experience in conducting knowledge transfer sessions.

Responsibilities

• Create and maintain data storage structures for analytics solutions.
• Automate data pipelines for data preparation and transformation.
• Support and enhance dashboards and reports.

Skills

Data Storage

Data Preparation

Automation

Knowledge Transfer

Tools

Azure Storage

Azure Data Lake

Azure Databricks

Azure Synapse

SQL

Python

Azure Data Factory

Power BI

Job description

Job Description:

• Creating, enhancing, maintaining, and supporting structures for storage of data in formats that are suitable for consumption in analytics solutions.
• Automation of data pipelines used to ingest, prepare, transform, and model data for use in analytics products.
• Creating, enhancing, maintaining, and supporting dashboards and reports.
• Creating, enhancing, maintaining, and supporting analytics environments and implementing new technology to improve performance, simplify architecture patterns, and reduce cloud hosting costs.
• Knowledge transfer sessions and documentation for technical staff related to architecting, designing, and implementing continuous improvement enhancements to analytics solutions. These sessions will be held as needed and on a case by case basis that involve walkthroughs of documentation, code, and environment setups.

Experience and Skill Set Requirements:
Data Storage and Preparation - 35%:

• The candidate must demonstrate their experience with Azure Storage, Azure Data Lake, Azure Databricks Lakehouse, and Azure Synapse structures in real world implementations.

Data Pipelines - 35%:

• The candidate must demonstrate their experience with automating data pipelines using appropriate Microsoft Azure Platform/Technologies (SQL,Python, Databricks and Azure Data Factory).

Data Analytics - 15%:

• The candidate must demonstrate their experience with Power BI reports and dashboards.

Knowledge Transfer - 15%:

• The candidate must demonstrate experience in conducting knowledge transfer sessions and building documentation for technical staff related to architecting, designing, and implementing end to end analytics solutions.