Manager, Data Engineering and Analytics

RBC•Toronto, ON

1d•Onsite

About The Position

As a Manager, Data Engineering and Analytics within GFT's HR Information Technology, you'll be the technical driver delivering foundational data infrastructure and automated solutions that power incentive compensation and HR analytics. Working closely with HR partners, solution architects, and development teams, you'll translate operational requirements into robust data pipelines, data models, and automated workflows moving beyond analysis into building scalable data solutions. You'll combine business acumen with hands-on engineering to design and implement the data layer that enables AI/ML initiatives, advanced analytics, and data-driven decision-making. This role requires strong technical leadership, business understanding, and the ability to deliver data solutions that directly enable business outcomes.

Requirements

5–7 years of professional experience in data engineering, data analytics, business analysis, or related role with demonstrated technical and analytical capabilities
Advanced technical expertise – Mastery in writing complex, high-performance SQL with query optimization, indexing strategies, and execution plan analysis; ability to embed optimized SQL into Python and scripting languages; proven ability to architect, build, and deploy end-to-end data pipelines with tools like dbt or Airflow
Data design & modeling – Experience designing dimensional models, star schemas, and other data structures that support analytics and reporting; comfortable with both batch and real-time data ingestion patterns
AI/ML literacy and data-driven thinking – Understanding of how AI/ML systems work, including good training data, model validation approaches, and business rule translation; demonstrated drive to identify manual processes and replace them with scalable, automated solutions
Business acumen & communication – Proven ability to analyze and redesign business processes; translate complex technical concepts for diverse audiences; excellent stakeholder management and clear written communication skills

Nice To Haves

Experience with advanced data and analytics platforms (Databricks, Hadoop, MongoDB, Snowflake, BigQuery, Redshift)
Financial services or regulated operational environment exposure and experience with enterprise HR/compensation systems (Workday, SAP S4/HANA, SAP SuccessFactors)
Greenfield or platform-building project experience with Agile Scrum delivery
Data engineering or analytics certifications (AWS Data Engineer, Google Cloud Data Engineer, or equivalent)

Responsibilities

Lead End-to-End Business & Systems Analysis for data engineering and AI-driven projects conduct requirements gathering, gap analysis, process mapping, and develop functional specifications that translate operational problems into data solutions
Design & Validate Foundational Data Layers – Partner with engineers to define data architecture, design data models, ensure data quality, map data lineage, and validate that data pipelines feed accurate, trustworthy information to ML models and analytics platforms
Build & Optimize Data Pipelines – Write SQL, design ETL/ELT workflows, and implement automated data pipelines to consolidate siloed data, replace manual processing, reduce errors, and improve efficiency across compensation and HR systems
Query & Analyze Data Independently – Write production-quality SQL to explore data from multiple sources, profile data quality, identify integration needs, and validate data integrity across complex systems
Support AI/ML Solution Development – Translate business rules and operational logic into structured data requirements, validate model outputs for accuracy and reliability, and ensure data solutions reflect business intent and governance standards
Design Automated Data Processing Workflows – Architect and implement automated solutions to replace manual processing, enable real-time analytics, and support scalable reporting across multiple data sources
Bridge Business & Technical Teams – Serve as connective tissue between operational stakeholders, business teams, and engineering—translating process knowledge into technical specifications and communicating progress to non-technical audiences
Document Data Infrastructure & Governance – Produce clear technical documentation (incl. data dictionaries, process flows, system maps, business rules, data lineage) that provides institutional memory and supports governance as the data platform scales
Identify Process & Efficiency Opportunities – Continuously analyze business processes to identify automation opportunities and recommend data-driven improvements that leverage analytics and AI capabilities

Benefits

A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable
Leaders who support your development through coaching and managing opportunities
Ability to make a difference and lasting impact
Work in a dynamic, collaborative, progressive, and high-performing team
A world-class training program in financial services
Flexible work/life balance options
Opportunities to do challenging work
Opportunities to take on progressively greater accountabilities
Opportunities to building close relationships with clients
Access to a variety of job opportunities across business and geographies