Lead Data Engineer / Data Platform Lead

Princeton IT Services, Inc; Toronto; On-site; CAD 120,000 - 180,000; Full time

Job summary

Princeton IT Services, Inc. in Toronto, Canada, is seeking a Lead Data Engineer / Data Platform Lead to drive architecture, governance, and analytics enablement. You will implement large-scale data pipelines across Hadoop/Databricks, unify diverse data sources, and mentor engineers while partnering with product and data science teams.

The role emphasizes GenAI/LLM-enabled data platforms, enterprise data strategy, and long-term platform sustainability, with onsite collaboration in Toronto.

Qualifications

• 8+ years of data engineering, big data analytics, or enterprise data platform experience.
• 2+ years in a lead or technical leadership role.
• Experience with cloud data platforms (Azure/AWS) and Databricks/Snowflake.
• Strong SQL and Python data engineering experience.
• GenAI/LLM data pipelines exposure is a plus.

Responsibilities

• Ingest, transform, aggregate, and process large datasets to enable analytics and downstream consumption.
• Design, build, and maintain robust data pipelines across Hadoop/Databricks and enterprise platforms with high data quality and availability.
• Drive data unification by integrating multiple data sources into a governed analytics foundation.
• Analyze high volumes of data to produce insights and actionable business recommendations.
• Apply metrics and benchmarks to evaluate solution effectiveness and drive improvements.
• Partner with Product Managers, Data Science, Platform Strategy, and Tech teams to translate requirements into scalable solutions.
• Act as a technical bridge between business, analytics, and engineering teams, articulating architecture decisions clearly.
• Ensure alignment of data solutions with business and customer outcomes, and identify innovation opportunities.
• Deliver proofs of concept, prototypes, and pilots aligned to near-term and future needs.
• Integrate new data assets to enhance platforms, products, and services.
• Provide leadership and mentorship to data engineers and analysts, setting engineering standards.

Skills

Python

Pandas

NumPy

PySpark

Impala

SQL

Data Modeling

Airflow

Azure

Databricks

CI/CD

Tools

Airflow

NiFi

Azure Data Factory

Databricks

Snowflake

Hadoop

Job description

Job Title: Lead Data Engineer / Data Platform Lead

Location: Toronto, Canada (onsite 5 days)

Summary: A senior and strategic Lead Data Engineer / Data Platform Lead role. In addition to hands‑on engineering, it emphasizes technical leadership, enterprise architecture, analytics enablement, stakeholder management, innovation, and long‑term platform strategy. It also introduces preferred experience in GenAI/LLM‑enabled data platforms, making it broader in scope than the first role.

Job Summary

• Lead the ingestion, transformation, aggregation, and processing of large‑scale datasets to enable advanced analytics and downstream consumption.
• Design, build, and maintain robust, scalable data pipelines across Hadoop/Databricks and enterprise data platforms, ensuring high standards of data quality, reliability, performance, and availability.
• Drive data unification initiatives, integrating multiple structured and semi‑structured data sources into a cohesive, governed analytical foundation.
• Manipulate and analyze high volume, high velocity, and high dimensional datasets using modern big data frameworks and/or cloud‑native applications.
• Analyze large volumes of transactional and product data to produce insights and actionable recommendations that support business growth and value realization.
• Apply metrics, measurement frameworks, and benchmark techniques to evaluate solution effectiveness and drive continuous improvement.
• Partner with Product Managers, Data Science, Platform Strategy, and Technology teams to understand analytical and data requirements and translate them into scalable engineering solutions.
• Act as a technical bridge between business, analytical, and engineering teams, clearly articulating architecture decisions, trade‑offs, and implementation approaches.
• Enable alignment across stakeholders to ensure data solutions are directly tied to business and customer outcomes.
• Identify innovation opportunities and deliver proofs of concept, prototypes, and pilot solutions aligned to near‑term and future business needs.
• Integrate new and emerging data assets that enhance existing platforms, products, and services, strengthening overall value propositions.
• Gather and synthesize feedback from clients, product, engineering, and sales teams to inform new solutions and product enhancements.
• Provide technical leadership, guidance, and mentorship to data engineers and analysts, setting standards for engineering quality, scalability, performance, and maintainability.
• Promote best practices in data modeling, pipeline design, performance optimization, and data governance.
• Influence engineering standards, architectural consistency, and long‑term platform sustainability.

All About You

Technical Skills & Experience

• Strong proficiency in Python, including Pandas, NumPy, PySpark, with hands‑on experience using Impala.
• Proven experience working on Hadoop‑based platforms, performing large‑scale data extraction, transformation, and processing.
• Strong SQL skills and experience working with both relational and distributed data stores.
• Experience with enterprise data platforms and business intelligence ecosystems.
• Hands‑on experience with ETL/ELT and data integration tools, such as Apache Airflow, Apache NiFi, Azure Data Factory.
• Experience in data modeling, querying, data mining, and reporting over large volumes of granular data.
• Exposure to machine learning concepts and analytical techniques used in advanced data solutions and feature calculations and model serving is a big plus.
• 8+ years of experience in data engineering, big data analytics, or enterprise data platforms, including 2+ years in a lead or technical leadership role.
• Experience working with cloud‑based data platforms (Azure/AWS, Databricks/Snowflake), including data lakes, distributed compute, and storage services.
• Experience implementing CI/CD pipelines and DevOps practices for data engineering workflows.

GenAI / LLM Skills (Preferred)

• Experience enabling GenAI/AI products through scalable, reliable data ingestion and transformation pipelines (batch and streaming).
• Exposure to unstructured and semi‑structured data processing (documents, logs, text) and building curated datasets for downstream consumption.
• Strong understanding of data governance, privacy, and security requirements when using enterprise data with AI (PII handling, access control, auditability).
• Familiarity with operationalizing AI data workflows (monitoring, data quality checks, reproducibility, and cost‑aware scaling in cloud environments).

Analytical & Business Acumen

Strong experience collecting, standardizing, and summarizing diverse datasets while identifying patterns, inconsistencies, and data quality issues.

Solid understanding of how analytics, metrics, and visualization support business decision making.

Ability to comprehend complex operational systems and deliver scalable analytics and information products to a global user base.

Ways of Working

• Comfortable operating in a fast‑paced, delivery‑driven environment, both as a hands‑on contributor and a technical leader.
• Ability to move seamlessly between business, analytical, and technical contexts, communicating clearly with diverse audiences.
• Demonstrates Mastercard's DQ values, with a collaborative, inclusive, and customer‑centric mindset.