Analytics Data Engineer - 82002

Line of Business: Treasury/Finance

Duration: 6 months

Location: Remote or Hybrid - Downtown Toronto [candidate needs to be able to go into the office for tech issues or special occasions.]

Role Responsibilities Include (but are not limited to):

• Data Collection and Preparation: Gather data from multiple sources and preprocess it to eliminate errors, inconsistencies, and ensure quality.
• Statistical Analysis: Apply statistical modeling techniques to identify patterns and relationships, leveraging methods such as hypothesis testing, regression, and clustering.
• Leverage SQL, Python, or R to uncover patterns, trends, and relationships.
• Interpret these findings to provide insights that address specific business challenges, such as identifying customer behavior or optimizing operations.
• Create visualizations using tools like Tableau, Power BI, or matplotlib. These visualizations, along with detailed reports, help stakeholders understand complex data and make data-driven decisions.
• Bridge the gap between technical data and business needs, enabling organizations to improve efficiency, reduce costs, and enhance customer satisfaction
• Incorporate advanced techniques like predictive modeling and machine learning to address complex challenges.

Must-Have Skills:

Programming & Tools

• Strong programming skills in Python, SAS, and SQL.
• Experience with Power BI, DAX, and M Code for dashboarding.
• Proficiency with MS 365 Suite: Office, Power Automate, SharePoint, OneDrive.

Database & Data Engineering

• Advanced configuration of SQL Server for high-throughput analytical workloads, including memory allocation and parallel query execution.
• Design and implementation of partitioned tables, indexed views, and columnstore indexes to support large-scale data operations.
• Deep understanding of SQL Server recovery models (Simple, Full, Bulk-Logged), including backup/restore strategies, log management, and disaster recovery planning.
• Development of robust ETL pipelines to extract, transform, and load data from IBM Netezza/Hadoop, ensuring efficient handling of large datasets.
• Experience with data staging, incremental loads, and change data capture techniques.

Data Architecture

• Experience with star/snowflake schemas, fact/dimension modeling, and slowly changing dimensions.
• Design and implementation of batch processing pipelines using Python and SQL.
• Solid understanding of RDBMS, NoSQL, and data file formats (CSV, Parquet, JSON).
• Proficient in translating business requirements into scalable data models.
• Cloud & Big Data
• Strong experience with AWS (Redshift, Glue, MLOps).

Nice to Have Skills:

• Previous Data Analyst Experience

Interviews:

• 1st round - Reporting Manager
• 2nd Round - Hiring Manager