About this role
About the Role
Transformation Engineer with hands-on software development, AI-enabled engineering, and modern quality practices to build intelligent solutions for data-intensive platforms and applications. You will accelerate delivery, improve reliability, and help evolve QA into an engineering-led quality model. What You'll Do
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Design and build scalable engineering solutions that use AI and automation to improve software delivery, testing, and platform reliability.
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Develop production-grade frameworks, services, and utilities in Python and related technologies to support intelligent validation, automation, and quality controls.
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Partner with engineering teams to embed quality engineering and validation practices early in the development lifecycle and drive shift-left transformation.
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Apply AI/ML techniques to improve test generation, coverage optimization, anomaly detection, defect identification, and engineering insight.
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Build and enhance data and platform validation capabilities across ETL pipelines, APIs, microservices, and streaming architecture.
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Integrate automation and intelligent quality checks into CI/CD pipelines to support continuous testing, quality gates, and release confidence.
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Help modernize legacy QA approaches into developer-aligned quality engineering frameworks and scalable engineering practices.
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Collaborate across Technology, Risk, Compliance, Operations, and Emerging Tech to drive consistent engineering and quality outcomes.
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Provide clear reporting and insights on engineering quality, risks, and release readiness, leveraging AI where appropriate. What We're Looking For
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8+ years of experience in software engineering, data platforms, ETL, or large-scale distributed systems.
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Strong hands-on development experience, particularly in Python, with ability to build reusable frameworks, services, and automation solutions.
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Experience applying AI/ML or intelligent automation in engineering workflows, especially in validation, testing, optimization, or anomaly detection.
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Strong knowledge of data systems, including ETL, batch/streaming pipelines, APIs, and microservices.
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Advanced experience with SQL and relational databases such as Oracle and PostgreSQL.
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Experience with modern automation and quality tools, including Playwright and/or Selenium, with a strong understanding of how quality engineering supports software delivery.
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Familiarity with CI/CD, DevOps, and continuous quality engineering practices.
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Demonstrated ability to influence teams to adopt quality‑first and shift‑left practices across design, development, and deployment.
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Familiarity with self-healing automation, intelligent orchestration, or AI-assisted validation frameworks. Nice to Have
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Strong understanding of how intelligent test orchestration supports DevOps and continuous quality gates.
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Familiarity with AI‑ or ML‑based approaches for data quality assurance including model validation, drift detection, or intelligent checks.
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Ability to act as a thought partner on how AI can continuously improve engineering and quality outcomes across platforms.
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Awareness of AI governance, explainability, and responsible AI principles, particularly in regulated or risk‑sensitive environments.
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Financial Services Background – Experience with analytical workflows, financial products, or regulatory processes Compensation & Benefits
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Hybrid flexibility to work from Chicago or Toronto offices with strong work-life balance.
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Competitive compensation, comprehensive benefits, and opportunities for training, certifications, conferences, and career growth.
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Innovation-driven culture with AI-enabled engineering approaches and modern quality practices.