Lead Data Engineer / Data Platform Lead

Job Title: Lead Data Engineer / Data Platform Lead

Location: Toronto, Canada (onsite 5days)

Summary: This is a more 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:

Data Engineering Lead

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.

Innovation & Value Creation

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.

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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 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 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, 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.