Python Data Engineer

Job Title – Python Data Engineer

Location – Chicago, IL || Naperville, IL - Hybrid

Duration – 12+ months C2H

Interview – Video

Job Description –

The focus will be data connections, transformation and pipeline development for our AI models in Python on local systems to be migrated to AWS.

Design, develop, and maintain scalable data pipelines using Python and PySpark.

Integrate and transform data from multiple enterprise systems into model-ready datasets.

Build and manage feature engineering pipelines for machine learning and GenAI applications.

Support deployment, monitoring, and maintenance of machine learning and LLM-based solutions.

Collaborate directly with business stakeholders to understand data requirements and translate business needs into technical solutions.

Analyze and validate data quality, identify anomalies, and troubleshoot pipeline issues.

Convert legacy SQL-based processes into modern Python/PySpark frameworks.

Partner with Data Science teams to operationalize models and deliver business outcomes.

Contribute to cloud migration and modernization initiatives within AWS environments.

Participate in agile, pod-based delivery teams consisting of business, technology, data, and modeling stakeholders.

DEEPER DIVE DESCRIPTION

Uses advanced analytical algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) and modern data engineering and MLOps capabilities to ingest, integrate, process, and analyze large sets of structured and unstructured data to obtain insights. Designs and constructs new processes for connecting data and AI solutions, engineering data, and operationalizing models. Leverages big data technology and AI platforms to design solutions that deliver smarter business decisions, improve customer experience, and drive productivity. Collaborates with other data, analytics, engineering, and AI professionals and teams to optimize, refine, deploy, monitor, and scale analysis into mature AI and analytics solutions.

• Plays an active role in the advancement of innovative data, AI, and MLOps strategies to understand consumer trends and address business problems.
• Uses data mining and data engineering to ingest, integrate, transform, and prepare usable data from valuable data sources to assess feasibility of AI/ML solutions for improved processing and usage of organization data.
• Conducts large-scale analysis of information to Client patterns and trends by combining different modules and algorithms and establishing scalable connections between data, AI/ML models, and downstream applications.
• Uses analysis to provide recommendations and advice for business leaders to maintain market competitiveness.
• Investigates additional technologies and tools for developing innovative data solutions and operationalizing AI/ML models through scalable MLOps capabilities for business stakeholders.
• Collaborates with the product team and partners to understand and provide data-driven decision making, business planning and future roadmap.
• Focus is primarily on business/group within BMO; may have broader, enterprise-wide focus.
• Exercises judgment to identify, diagnose, and solve problems within given rules.
• Works independently on a range of complex tasks, which may include unique situations.
• Broader work or accountabilities may be assigned as needed.

Foundational level of proficiency

• Deep learning.
• Machine learning.
• Trust, bias and ethics.
• Creative thinking.
• Critical thinking.

Intermediate level of proficiency

• Mathematics, statistics & operations research.
• AWS tool.
• Data engineering and integration.
• Computational thinking and programming.
• Data wrangling.
• Data preprocessing.
• MLOps and AI/ML model deployment and monitoring.
• AI platforms, connectivity, and integration.
• Complex problem solving.
• Analytical acumen.
• Creative reasoning.
• Verbal & written communication skills.
• Collaboration & team skills.
• Analytical and problem-solving skills.
• Influence skills.
• Data driven decision making.
• Technical proficiency gained through education and/or business experience.

Required skills

5+ years of Data Engineering, ML Engineering, or Data Platform experience (3+ years considered for strong candidates).

Strong Python development experience.

Hands-on experience with PySpark and large-scale data processing.

Advanced SQL skills, including complex query development and optimization.

Experience building and supporting data pipelines in enterprise environments.

Experience working with AWS services.

Strong understanding of data modeling, ETL/ELT processes, and data integration practices.

Data wrangling and reprocessing

MLOps and AI/ML model deployment and monitoring.

Preferred Qualifications

Experience with GenAI, LLMs, AWS Bedrock, Copilot, or similar AI platforms.

Financial Services, Banking, or Commercial Banking experience.

Experience supporting machine learning model deployment and monitoring.

Experience working with Netezza, SQL Server, or Sybase databases.

Previous consulting or client-facing experience.

Worksite Address options

Chicago, IL, 60606

OR

Naperville, IL 60563

hybrid- 2-3 days onsite

INTERVIEW SHCEUDULE

2 step interviews, virtual

contract to hire- building and adding to a long-term team

Will be aligned in Pods with someone senior on Gordon\'s team to learn and get up and running quickly

Success Factors

Demonstrates strong intellectual curiosity and problem-solving skills.

Understands business context behind data and analytics solutions.

Comfortable working in ambiguous, evolving environments.

Able to contribute quickly within a highly collaborative team structure.

The data pipelines and AI models his team builds help

BMO

Better understand client financial health

Identify risks earlier

Improve credit and lending decisions

Deliver more personalized client recommendations

Provide relationship managers with better insights

Reduce manual research and analysis