AI Data Engineer (Snowflake + AI)

Job ID: 4087

Welcome to Group Technology, where we pride ourselves on engineering solutions and direct Nordea’s transformation by providing a holistic technological view and structured understanding of the bank, and its surrounding environment to enable the Customer Vision and the Business Strategy.

Nordea is a place where traditions meet tomorrow. We’re not just a bank, we’re a tech employer on a mission to evolve finance securely and responsibly. Together, we impact millions of people’s daily lives by ensuring they can access our solutions anytime, anywhere, while safeguarding their personal data and wealth. Join us in making an impact on the banking industry.

About Our Team

The main goal of this role is to help us build and establish AI processes around our data. We work on financial and regulatory datasets in Snowflake and we want to start using AI meaningfully — not as a productivity gimmick, but as a serious part of how we work with data: surfacing insights, assisting engineers, and empowering business users who interact with our data platform.

You’ll own this space. That means figuring out what’s worth building, what’s feasible within a regulated banking environment, and then actually building it — from Snowflake Cortex integrations to LLM‑assisted tooling for the engineering team to AI‑powered features in our Streamlit self‑service platform. Like everyone on the team, you work cross‑functionally — from understanding what the data represents in a banking context, through development and testing, to owning CI/CD and deployment.

We don’t expect you to arrive with every answer. The team will support you with domain context, existing patterns, and shared knowledge — and you’ll have people around you who are invested in your growth, not just your output.

Main responsibilities in this role:

• Define and establish AI processes around our data — identify where AI adds real value, design the approach, and own the implementation
• Must have a good understanding of MCPs, Context Engineering and relevant tools that supports LLM integrations
• Must have an experience in productionised Agentic AI solutions with market/Industry standards decision making Agents
• Build LLM‑powered features into our Streamlit‑in‑Snowflake platform (e.g. natural‑language query interfaces, anomaly detection, schema explanation for business users in Finance and Risk)
• Work with Snowflake Cortex (LLM functions, Cortex Search, Cortex Analyst) to bring AI capabilities directly into our Snowflake environment
• Design RAG pipelines over our structured and semi‑structured data — metadata catalogs, transformation configs, code repositories
• Develop AI‑assisted tooling for the engineering team: SQL review assistants, documentation agents, test generators
• Contribute to team AI practices — how we use Copilot, what context we maintain, how we evaluate output quality
• Build and maintain Snowflake transformation pipelines alongside AI work — stored procedures, Snowpark (Python/Scala), Dynamic Tables
• Engage in requirement analysis — understand domain and data context before building solutions
• Own CI/CD for your deliverables end‑to‑end

Must have:

• 8+ years in data engineering or ML engineering with production Snowflake experience
• Strong SQL and Snowpark (Python or Scala)
• At least 1 year working with LLMs in production — API integrations, structured outputs, tool use, retrieval
• Practical experience with: Snowflake Cortex, Anthropic API, OpenAI API, or Azure OpenAI
• Airflow, Bitbucket, CI/CD — end‑to‑end ownership, not just code delivery

Nice to have:

• Agentic frameworks (LangGraph, Claude Agent SDK)
• Streamlit in Snowflake
• LLM evaluation frameworks (LLM‑as‑judge, golden sets, prompt regression testing)
• Financial services or banking domain knowledge — credit risk, finance, or regulatory data background. You need to understand what the data means to build something useful on top of it

Mindset:

• Comfortable working across the full delivery lifecycle — analysis, development, testing, deployment
• Thinks about cost, latency, and determinism when designing AI features

Working with AI:

• Genuine fluency with LLMs — understands how they work, where they’re useful, and where they fail
• Actively tracks the AI tooling market: new models, coding assistants, Snowflake Cortex updates, agentic frameworks
• Knows how to maintain context files and prompt standards so the whole team benefits from AI tooling, not just the person who figured it out
• Treats AI output as a starting point — reviews, tests, and owns what gets committed

What We Offer

Collaboration. Ownership. Passion. Courage. These are the values that guide us in how we work and how we make decisions – and that we imagine you share with us.

People are driven by many different factors. For some, it’s to take their career to the next level. For others, it’s to break new ground within their area of expertise – in other words, with us, you will always move forward.

A culture that fosters performance and growth in one of the largest Nordic banks, offering various opportunities to evolve, develop and learn from brilliant colleagues with diverse backgrounds in a vibrant working environment.

Hybrid working model – we believe in the value of bringing people together and at the same time we embrace the freedom of flexibility.

Diversity and inclusion are a natural part of our daily work. We know that an inclusive workplace is a sustainable one. We genuinely believe that our diverse backgrounds, experiences, characteristics and traits make us stronger together. Every day we strive to find new ways to improve diversity and inclusion within our community e.g. we have signed the European Diversity Charters in the countries where we operate to show our commitment and engage with others to continue learning and improving.