Head, Data & Intelligence Engineering

About the Role

The Head, Data & Intelligence Engineering owns the data platforms, analytics, and AI capabilities that power decision-making and personalized customer experiences at Polaris Bank. This is a leadership role directing four specialist disciplines — Data Engineering, Analytics & BI, Data Science & AI, and Data Governance — through dedicated leads and engineers, not as a hands-on individual contributor across all four.

Key Responsibilities

Data Engineering Oversight

• Direct the design of scalable, reliable data pipelines, warehouses, and ETL infrastructure
  
• Set standards for data modeling and infrastructure architecture used across the bank's data platforms
  

Analytics & BI Oversight

• Ensure the analytics function delivers dashboards, reports, and self-service tools that drive data-driven decisions across the bank
  
• Set standards for data visualization and reporting consistency
  

Data Science & AI Oversight

• Direct the development of AI/ML models supporting personalization, fraud detection, credit scoring, and operational optimization
  
• Ensure model performance, fairness, and reliability are validated before production deployment
  

Data Governance Oversight

• Ensure data quality, privacy, and metadata management practices are enforced across all data assets
  
• Own the bank's data governance framework and ensure regulatory compliance in data handling
  

Core Competencies

• Data platform strategy and technical leadership across engineering, analytics, and data science
  
• Working fluency in ML/AI model lifecycle management and MLOps practices
  
• Strong grounding in data privacy and regulatory compliance (NDPR and applicable banking data regulations)
  
• Stakeholder management across technology, risk, and business functions
  

Familiarity With Tools (used by the function's teams)

Python, SQL, Apache Spark, Airflow, Kafka, Databricks, Snowflake, AWS Glue, dbt; Power BI, Tableau, Looker, Metabase; TensorFlow, PyTorch, Scikit-learn, MLflow, SageMaker, Kubeflow; Collibra, Informatica

Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, or related field; advanced degree an advantage
  
• Demonstrated track record leading data engineering, analytics, or data science functions, ideally in banking or financial services