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Iris Software is looking to hire a Data Engineer for a full-time opportunity in Toronto, ON (hybrid position). Please respond back with your most recent resume if you would be interested...! Job Title: Data Engineer Location: Toronto, ON (2 days onsite) Full-time with Iris Software for one of the banks in downtown Toronto The client is looking for: - feature engineering - data wrangling - model automation - Tool stack probably in this order – Python, PySpark, Glue, Sagemaker, SAS We are seeking a highly skilled Data Engineer to help build a net-new PySpark data engineering capability from the ground up. The initial focus of this role will be designing and developing net-new feature engineering pipelines to support downstream data science initiatives. As the platform capability matures, you will play a critical role in a large-scale modernization effort, tasked with translating and migrating legacy SAS-based workloads into a modern, scalable PySpark environment managed via Anaconda. The ideal candidate possesses strong core data engineering expertise, experience building data preparation pipelines, and the analytical ability to reverse-engineer legacy business logic into optimized open-source code. Key Responsibilities Foundation & Feature Engineering : Lead the foundational setup of Python/PySpark development environments, actively managing complex package dependencies and virtual environments using Conda/Anaconda. Pipeline Development : Design, build, and deploy net-new data pipelines focused heavily on feature engineering and data preparation to feed downstream machine learning and analytics use cases. Code Translation : Analyze and reverse-engineer existing SAS programs (developed by business users and data scientists) to accurately extract business rules, data transformations, and calculations. Platform Modernization : Translate extracted SAS logic into efficient, scalable, and functionally equivalent PySpark code. Performance Tuning : Optimize PySpark code for performance, scalability, and maintainability within distributed data processing environments. Stakeholder Collaboration : Collaborate closely with business users and data scientists to clarify requirements, validate feature outputs, and resolve discrepancies during the migration process. Quality Assurance : Perform robust unit testing, data reconciliation, and automated validation to ensure absolute data parity between legacy SAS outputs and the new PySpark pipelines. Documentation : Document technical designs, code lineage, testing results, and migration methodologies for future team scaling. Required Qualifications 5+ years of hands-on experience in data engineering, data pipeline development, or building data platforms. Strong programming expertise in Python and PySpark for distributed data processing. Proven experience building data pipelines specifically for feature engineering, data curation, and advanced data preparation. Ability to read, interpret, and reverse-engineer legacy SAS code (such as SAS data steps, procedures, and macros) to extract complex business logic. (Note: Deep SAS development experience is a plus, but the ability to translate it is the core requirement). Experience establishing and managing Python environments, ensuring reproducibility, and handling library dependencies using Anaconda/Conda. Advanced SQL skills and experience working with large-scale structured datasets. Experience with code migration, platform modernization, or translating legacy codebases into modern open-source stacks. Strong analytical, problem-solving, and communication skills, with a track record of successfully interfacing directly with business stakeholders. Preferred Qualifications Experience working with cloud-based data platforms (Azure, AWS, or GCP). Familiarity with version control tools (Git) and collaborative development workflows. Experience with CI/CD pipelines, automated testing, and MLOps principles. Exposure to financial services, banking, or other highly regulated industries. Knowledge of data governance, data cataloging, and data quality best practices. Key Skills Python & PySpark Feature Engineering & Data Pipelines Anaconda / Conda Environment Management SAS Interpretation & Code Translation Distributed Computing & Spark Optimization SQL & Data Reconciliation Legacy Platform Modernization Agile Delivery About Iris Software Inc. With 4,000+ associates and offices in India, U.S.A. and Canada, Iris Software delivers technology services and solutions that help clients complete fast, far-reaching digital transformations and achieve their business goals. A strategic partner to Fortune 500 and other top companies in financial services and many other industries, Iris provides a value-driven approach - a unique blend of highly skilled specialists, software engineering expertise, cutting-edge technology, and flexible engagement models. High customer satisfaction has translated into long-standing relationships and preferred-partner status with many of our clients, who rely on our 30+ years of technical and domain expertise to future-proof their enterprises. Associates of Iris work on mission-critical applications supported by a workplace culture that has won numerous awards in the last few years, including Certified Great Place to Work in India; Top 25 GPW in IT & IT-BPM; Ambition Box Best Place to Work, #3 in IT/ITES; and Top Workplace NJ-USA. Kind regards, Amarpreet Singh 200 Bay Str. Toronto, ON, M5H4E9 200 Metroplex Drive, Suite #300 Edison, NJ 08817 amarpreet.singh@irissoftware.com | www.irissoftware.com Iris Software ranked 38th among 1200 companies spanning 20+ industries in the country to become India’s Best Companies to Work For 2022!
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.