We’re looking for a hands-on leader to take ownership of our data and analytics landscape.

This isn’t a pure management role – we need someone who can roll up their sleeves, dive into AWS environments, and still have the strategic view to guide a growing data team.

The ideal candidate combines strong technical expertise in data engineering and analytics with a solid understanding and genuine passion for Artificial Intelligence (AI) — including how AI can enhance data-driven decision-making and operational efficiency in the financial domain.

In this position you’ll:

• Shape the company’s overall data strategy and decide how we store, move, and use data across the business.
• Lead a small but growing group of engineers, analysts, and BI specialists, making sure they have the right tools and direction.
• Stay close to the technology – designing and reviewing data pipelines, checking performance, and being the go-to person for AWS data solutions like Redshift, Glue, Athena, S3, and others.
• Work with business leaders (finance, risk, compliance) to turn their questions into clear, data-driven answers.
• Identify opportunities to integrate AI/ML into data workflows and business processes.
• Establish best practices for data governance, quality, and security in a financial services org.
• Drive automation and efficiency through modern data and AI tools
  
  

This is a role for someone who enjoys building as much as leading. If you get a kick out of solving tricky performance problems one day, then presenting a roadmap to executives the next, you’ll fit right in.

Requirements:

We’re after someone with solid experience:

• A deep background in databases, BI, and analytics (7+ years).
• Several years leading or directing data teams, ideally in finance or a regulated industry.
• Hands-on skills in SQL, Python, ETL, and cloud data platforms – not just theory.
• Strong AWS knowledge – you should feel at home in that ecosystem.
• Strong understanding and enthusiasm for AI and ML concepts, with the ability to identify and implement practical applications within data and analytics environments.
• Experience in applying AI technologies — such as LLM, predictive modeling, or AI-driven automation — in financial or data-intensive settings.