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.
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