Senior Data Engineer - Financial

HRB; Toronto; On-site; CAD 120,000 - 160,000; Full time

Job summary

HRB is seeking a Senior Data Engineer to design, build, and optimize enterprise-scale data platforms that support financial reporting, planning, forecasting, and strategic decision-making across the business.

The ideal candidate combines deep expertise in modern data engineering with strong analytical capabilities, collaborating with finance, operations, BI and data science teams to develop scalable data solutions that transform complex data into actionable insights.

Qualifications

• Hands-on experience with Apache Spark, AWS and Python in enterprise data environments.
• Proven track record designing and optimizing large-scale data pipelines and platforms.
• Experience with cloud-native data solutions and distributed processing systems.
• Strong understanding of data warehousing, ETL/ELT and data integration.

Responsibilities

• Design, develop and maintain scalable data pipelines ingesting and transforming large data volumes.
• Build cloud-based data platforms and data lake architectures using AWS services.
• Develop ETL/ELT processes with Python and Apache Spark for analytics and forecasting.
• Collaborate with Finance and business stakeholders on reporting requirements.
• Design dimensional data models to support financial analysis and operational reporting.
• Ensure data quality, governance and reliability across enterprise assets.
• Perform data profiling and explore data to identify trends and opportunities.
• Work with analysts and data scientists to enable advanced analytics and decision support.
• Monitor and optimize data workflows for scalability and performance.
• Contribute to evolving data architecture standards and engineering practices.

Skills

Apache Spark

AWS

Python

Data pipelines

Data models

ETL/ELT

Data quality

EDA

Tools

Spark

Cloud-native data solutions

Job description

Position Overview

We are seeking a highly skilled Senior Data Engineer to join our growing Data & Analytics team within a leading environmental services organization. This role will play a critical part in designing, building, and optimizing enterprise-scale data platforms that support financial reporting, planning, forecasting, and strategic decision-making across the business.

The ideal candidate combines deep technical expertise in modern data engineering technologies with strong analytical capabilities and experience working with financial datasets. You will collaborate closely with finance, operations, business intelligence, and data science teams to develop scalable data solutions that transform complex data into actionable business insights.

Key Responsibilities

• Design, develop, and maintain scalable, high-performance data pipelines that ingest, transform, and deliver large volumes of structured and unstructured data.
• Build and optimize cloud-based data platforms and data lake architectures using AWS services and modern data engineering best practices.
• Develop robust ETL/ELT processes using Python and Apache Spark to support analytics, reporting, and forecasting initiatives.
• Partner with Finance and business stakeholders to understand reporting requirements and translate them into reliable data solutions.
• Design and implement dimensional and multi-dimensional data models that support financial analysis, budgeting, forecasting, and operational reporting.
• Ensure data quality, consistency, governance, and reliability across enterprise data assets.
• Perform data profiling and exploratory data analysis (EDA) to identify trends, anomalies, and opportunities for improving business performance.
• Collaborate with data analysts, data scientists, and business intelligence teams to enable advanced analytics and decision-support capabilities.
• Monitor, troubleshoot, and optimize data workflows to ensure scalability, efficiency, and performance.
• Contribute to the evolution of data architecture standards, best practices, and engineering processes.

Required Qualifications

• Strong hands-on experience with Apache Spark, AWS, and Python in enterprise-scale data environments.
• Proven track record designing, building, and optimizing large-scale data pipelines and modern data platforms.
• Experience developing and maintaining cloud-native data solutions and distributed data processing systems.
• Strong understanding of data warehousing concepts, ETL/ELT methodologies, and data integration practices.
• Excellent problem-solving skills and the ability to work with complex datasets in a fast-paced environment.
• Strong communication skills with the ability to collaborate effectively across technical and business teams.

Preferred Qualifications

• Experience working with financial data, including financial reporting, budgeting, forecasting, planning, revenue analysis, cost management, or other finance-related datasets.
• Strong proficiency in exploratory data analysis (EDA) and translating business requirements into effective analytical data models.
• Experience designing dimensional and multi-dimensional data models to support enterprise reporting and analytics.
• Solid understanding of statistics and its application to data analysis, forecasting, trend identification, and decision support.
• Familiarity with financial performance metrics, KPIs, and analytical frameworks used in corporate finance environments.
• Experience supporting advanced analytics, predictive modeling, or forecasting initiatives.

What Success Looks Like

In this role, you will help establish a trusted and scalable data foundation that enables better financial visibility, operational efficiency, and strategic decision-making. Your work will directly support the organization's ability to analyze business performance, forecast future outcomes, and drive data-informed growth within the environmental services sector.

Ideal Candidate Profile:
A technically strong data engineer who enjoys building scalable cloud-based data solutions and has experience working with financial data and analytics. You are equally comfortable engineering robust data pipelines, modeling complex business data, and partnering with stakeholders to deliver meaningful business insights.