Data Engineer

What You’ll Do

You'll consult with data scientists, the data and analytics teams, technology partners, business partners to create pipelines, governance and solutions that impact a variety of model ops, data and insight challenges across the North American Commercial Bank. You’ll wrap model ops and analytics in clean, repeatable, and automated processes. You’ll play a critical role in elevating business stakeholders’ confidence in our data. More specifically you will:

• Become the expert on the data sourced and used in the ML operations
• Transform structured and unstructured data for ML and Analytics programs.
• Ensure production behavioral scores and business metrics values align with expectations
• Identify and explain deviations in metrics proactively.  Set in motion paths for resolution
• Exercises judgment to identify, diagnose, and solve problems within given rules
• Clarify key business metrics.  Code and implement these metrics at scale
• Prepare modeling master data sets in collaboration with data scientists
• Create insights and visualizations which answer high impact business questions
• Prepare model validation documentation

Qualifications

• Consultative approach in engaging internal partners (e.g. data science, business, technology) desired
• Strong creative reasoning and computational thinking skills
• Clear verbal & written communication skills
• Extensive collaboration & team skills
• Experience impacting business transformation with data
• Proven experience building data pipelines in production for advanced analytics use cases
• Practical knowledge of software engineering concepts and best practices, inc. DevOps, DataOps, and MLOps
• Proven ability to write clean, maintainable, scalable, and production ready code
• Practical knowledge of software engineering concepts and best practices, inc. DevOps, DataOps, and MLOps
• Experience utilizing NLP to transform Unstructured Data
• Strong proficiency with SQL and its variations among different databases
• 4+ years production development experience
• 6+ years of relevant work experience as a Data Operations Engineer 
• 6+ years of hands on experience in building production ETL/ELT solutions for large scale data pipelines
• Familiarity with distributed computing frameworks (e.g. Spark, Dask), and analytics libraries (e.g. pandas, NumPy, matplotlib)
• Familiarity with Generative AI use, optimization, and tools (APIs, Vector DBs, Prompt Engineering)
• Familiarity with cloud platforms (e.g. AWS, Azure, GCP)
• 6+ years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.