Snowflake Data Engineer

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.

You’ll join a Large Data Foundation team in a banking environment responsible for building and maintaining transformation layers on Snowflake — turning ingested raw data into clean, governed, analytics‑ready datasets consumed for IRB risk model development and model use for risk parameters.

We work cross‑functionally — you won’t just write code handed to you in a ticket. You’re expected to understand the data you work on, contribute to requirements elicitation, own development, and involve yourself in all phases of testing, and take part in CI/CD and deployment processes. This role requires both analytical and development skill sets.

You know Snowflake well enough to choose the right tool for the job: when to write a SQL procedure, when Snowpark makes more sense, when a Dynamic Table is the answer, and when a simple view is enough. You avoid unnecessary complexity when simplicity works.

• Design and build transformation jobs in Snowflake — SQL stored procedures, Snowpark (Python/Scala), Dynamic Tables
• Engage in requirement analysis — understand the business and data context before writing code, not after
• Translate business and analytical requirements into clean, maintainable transformation logic
• Apply Snowflake best practices: clustering, micro‑partition awareness, query optimization, warehouse rightsizing
• Contribute to data modeling decisions — schema design, naming conventions, partitioning strategies
• Own CI/CD for your deliverables — branching, deployments, environment promotion via Bitbucket and Jenkins
• Write and maintain Airflow DAGs that orchestrate Snowflake transformation workloads
• Contribute to our data exchange layer (Database Roles, Secure Views, cross‑account sharing)
• Review code, pair with team members, contribute to team standards

• 8+ years in data engineering with at least 2 years hands‑on Snowflake in production
• Strong SQL — window functions, QUALIFY, MERGE, recursive CTEs, FLATTEN/LATERAL for semi‑structured data, performance tuning
• Snowpark (Python or Scala) — building transformation logic, understanding when it’s preferable to pure SQL
• Understanding of Snowflake execution model: micro‑partitions, clustering, pruning, query plan reading
• Python or Scala for pipeline logic and tooling
• Airflow — authoring DAGs, sensors, retries
• Git, Bitbucket, CI/CD — comfortable owning deployments, not just writing code
• Financial services or banking domain knowledge — need to understand what the data means, not just move it. Credit risk, finance, or regulatory reporting background is expected

• Snowflake Dynamic Tables — knows when to use them vs Tasks vs stored procedures
• Streams and Tasks for incremental processing patterns
• Snowpipe or Snowpipe Streaming
• Data Stage — for working alongside legacy pipelines
• Streamlit in Snowflake
• SAFe or scaled agile experience

• Comfortable working across the full delivery lifecycle — analysis, development, testing, deployment
• Chooses the simplest Snowflake feature that solves the problem — not the most impressive one
• Reads the query profile before optimizing, doesn’t guess
• Treats auditability, lineage, and access controls as part of the design, not an afterthought

• Actively uses AI coding tools as part of daily work — not occasionally
• Understands how LLMs work well enough to use them effectively: knows what context to provide, how to scope a prompt, and when to trust the output versus when to push back
• Keeps up with the AI tooling market — aware of what’s available, what’s improving, and what’s relevant for data engineering work

• Snowflake, Snowpark (Python/Scala), Dynamic Tables, Airflow, Data Stage (legacy interop), Streamlit in Snowflake, Bitbucket, Jenkins, IntelliJ/VS Code, Git Hub Copilot, Gemini Plugin, Cortex and more.

• Collaboration, ownership, passion, courage, and a culture that fosters performance and growth in one of the largest Nordic banks.
• Hybrid working model that balances flexibility with collaboration.
• Diversity and inclusion commitment, signed to the European Diversity Charters.

If this sounds like you, get in touch!