Solutions Architect

Iris Software's client one of the leading Bank in Canada is looking to hire for the following role

Title: Solution Architect

Role : Hybrid ( 2- 3 days onsite)

Job Description:

•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.