Director of Data & Analytics

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.