Job Title : 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. 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. 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 Client’s DQ values, with a team-oriented, inclusive, and customer centric mindset. Note: Skills which is highlighted are Mandatory for this position.

📌 Data Platform Engineer - C$140,000 - C$170,000 A Year (Toronto)
🏢 Clio
📍 Toronto