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. About our team This is a depth role. While everyone on the team builds on Snowflake, you're the person who sets the standard — for transformation patterns, performance, governance, and platform decisions. You know Snowflake deeply enough to know which feature to use, which to avoid, and why. You make architectural calls before anyone writes code and align Technology Product lead, and you're still happy to sit with a junior engineer on their PR afterwards. We work cross-functionally — senior engineers are expected to contribute across the full delivery lifecycle: from requirements elicitation and credit risk data domain understanding, through development and code review, to owning CI/CD standards and deployment practices for the team. You'll be joining a team that values knowledge sharing seriously — there's a strong culture of pairing, design reviews, and internal documentation. You'll both benefit from that and be expected to contribute to it. Main responsibilities in this role: Set technical standards for Snowflake transformations — when to use Dynamic Tables vs Tasks vs stored procedures vs Snowpark, and the tradeoffs of each Deep dive into Data and come up with technical solutions to align with Business requirements Contribute to and review requirement analysis — bridge domain knowledge and technical implementation Deep-dive performance work: query profiling, micro-partition analysis, clustering design, warehouse rightsizing, spill elimination Own data modeling decisions — schema design, partitioning strategy, incremental patterns, SCD approaches Lead governance: RBAC hierarchy, Database Roles, masking policies, row access policies, tag-based governance Design and evolve the data sharing architecture (Secure Views, Reader Accounts, cross-account sharing for downstream teams) Drive and own CI/CD standards for Snowflake: schemachange, Terraform, or equivalent integrated with Bitbucket/Jenkins Mentor mid-level engineers, run design reviews and brown-bags Partner with team lead on PI Planning, roadmap, and technical decisions Who you are Your background and skills include: Must have: 10+ years in data engineering, with at least 3 years Snowflake at meaningful production scale Key understanding of Data knowledge and be able to perform Data analysis and be able to design and lead Technical solutions SnowPro Core certified; SnowPro Advanced: Data Engineer strongly preferred Deep SQL mastery: query plan interpretation, QUALIFY, MATCH_RECOGNIZE, recursive CTEs, advanced VARIANT/OBJECT/ARRAY handling, window functions, lateral joins Snowpark (Python or Scala) — knows when it's the right call and when SQL is better Strong grasp of Snowflake internals: micro-partitions, clustering, pruning, result caching, warehouse credit consumption Dynamic Tables, Streams, Tasks — has used all three in production and can articulate when each is appropriate Governance: RBAC, Database Roles, masking policies, row access policies Data sharing: Secure Data Sharing, Reader Accounts, cross-region/cloud replication CI/CD for Snowflake: schemachange, Terraform, or equivalent — owns it, doesn't just use it Financial services or banking domain knowledge — credit risk, finance, or regulatory reporting. At senior level, domain understanding directly influences architectural decisions Nice to have: Credit risk functional knowledge Airflow in a Snowflake-heavy environment DataStage (legacy interop context) Snowflake Cortex, Snowpipe Streaming Mindset: Comfortable working across the full delivery lifecycle — analysis, architecture, development, CI/CD, deployment Treats the platform as a product — reliability, cost, and developer experience all matter equally Documents decisions, not just code — architecture records, naming conventions, runbooks Working with AI: Uses AI tooling confidently and sets an example for the team on how to do it well Understands context engineering — knows that good AI output starts with good context, and helps maintain the repo-level files that make that possible Tracks the market: aware of how AI tooling for data engineering is evolving and can have an informed opinion on what's worth adopting Recognises when AI-generated code is architecturally wrong even if it compiles — and explains why 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. 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.