Lead Data Platform Engineer

Location – Downtown Toronto (hybrid - minimum 3 days in a week)

Duration: 6 months with possible extensions

Key Responsibilities

• 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.

Advanced Analytics Enablement

• Manipulate and analyse high volume, high velocity, and high dimensional datasets using modern big data framework and/or Cloud native applications
• Analyse large volumes of transactional and product data to produce insights and actionable recommendations that support business growth and value realisation.
• Apply metrics, measurement frameworks, and benchmarking techniques to evaluate solution effectiveness and drive continuous improvement.

Cross Functional Collaboration

• 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 synthesise feedback from clients, product, engineering, and sales teams to inform new solutions and product enhancements.

Technical Leadership & Mentorship

• 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 modelling, pipeline design, performance optimisation, and data governance.
• Influence engineering standards, architectural consistency, and long term platform sustainability.

Technical Skills & Experience

• Strong proficiency in Python, including Pandas, Num Py, 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 modelling, 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 Dev Ops 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, standardising, and summarising diverse datasets while identifying patterns, inconsistencies, and data quality issues.
• Solid understanding of how analytics, metrics, and visualisation 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.