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Job Overview
Location
Toronto, Ontario, Canada
Job Type
Full-time
Category
Data Engineer
Date Posted
March 18, 2026
Full Job Description
đź“‹ Description
- • As a Data Engineer at Tiger Analytics Inc., you will play a pivotal role in architecting, designing, and implementing advanced analytics capabilities that drive business value for Fortune 500 clients. Your work will directly contribute to transforming complex data into actionable insights, enabling strategic decision-making across industries and reinforcing the company’s reputation as a trusted analytics partner recognized by Forrester and Gartner.
- • You will be responsible for building and optimizing large-scale data processing pipelines on AWS, leveraging Snowflake, Databricks, dbt, and Apache Spark to handle massive, complex datasets. This includes designing efficient data models, implementing ELT/ETL workflows, and ensuring data quality, reliability, and performance at scale—turning raw data into structured, usable assets for analytics and AI initiatives.
- • You will collaborate closely with cross-functional teams of data scientists, analysts, and consultants to support self-service dashboarding and visualization efforts, translating technical solutions into business outcomes. Your ability to communicate technical concepts clearly and work in dynamic, entrepreneurial environments will be key to delivering impact.
- • Tiger Analytics is a fast-growing, innovation-driven consulting firm with a global footprint and a culture of excellence. You’ll join a team of deep experts in data science, machine learning, and AI, working on high-impact projects that push the boundaries of what’s possible with data—offering exposure to cutting-edge technologies and real-world business challenges.
- • In this role, you will have the opportunity to shape the future of the company’s data analytics infrastructure, mentor junior engineers, and lead technical initiatives that scale across clients and geographies. You’ll grow your expertise in cloud-native data architectures, AI-enabled data pipelines, and enterprise-scale analytics—positioning yourself as a leader in the evolving data engineering landscape.
🎯 Requirements
- • 12+ years of overall industry experience specifically in data engineering with a heavy focus on the AWS Cloud stack and AI.
- • 8+ years of experience building and deploying large-scale data processing pipelines in a production environment.
- • Advanced proficiency in Python, SQL, and PySpark, including creating and optimizing complex data transformation pipelines.
- • Deep experience with Snowflake/Databricks on AWS, dbt, and distributed computing frameworks like Apache Spark.
- • Strong understanding of data warehouse (DWH) systems, ELT/ETL patterns, data modeling, and migration from traditional DWH to cloud-based data lakes or Snowflake.
- • Proven ability to work with unstructured datasets and build processes supporting data transformation, metadata, dependency, and workload management.
🏖️ Benefits
- • Significant career development opportunities as the company grows, with a high degree of individual responsibility in a small, challenging, and entrepreneurial environment.
- • Opportunity to work with Fortune 500 clients and contribute to high-impact analytics projects recognized by industry leaders like Forrester and Gartner.
- • Equal employment opportunities employer committed to diversity, inclusion, and fairness in hiring and workplace practices.
Skills & Technologies
About Tiger Analytics Inc.
Tiger Analytics is a leading product and services company specializing in artificial intelligence and advanced analytics. They help businesses across various industries, including retail, CPG, financial services, and healthcare, to solve complex problems and drive growth through data-driven insights. Their core offerings include building AI-powered solutions, developing machine learning models, and providing strategic consulting to optimize business operations, enhance customer experiences, and create new revenue streams. Tiger Analytics focuses on delivering measurable business impact by leveraging cutting-edge technologies and deep domain expertise to transform data into actionable intelligence.
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