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.