
Hover or tap a row for full statistics (EUR / month on this chart).
Salary analysis
Compared with the selected benchmark ("In Helsinki, Finland: Data Engineer"), this listing's salary midpoint is about 91% lower. The offer sits below the benchmark range (€3,000–€6,850). The listed pay band (€354–€500) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 14 comparable listings.
| Market | Lower bound (25th percentile) | Median | Upper bound (75th percentile) |
|---|---|---|---|
| Market Average: Data Engineer | €2,990/per month | €5,125/per month | €7,204/per month |
| In Helsinki, Finland: Data Engineer | €3,000/per month | €5,125/per month | €6,850/per month |
| Pay in our data — not quoted in ad (Mid-Level) | €354/per month | €427/per month | €500/per month |
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. About our team 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 involving in all phases of testing, and take part in CI/CD and deployment processes. Main responsibilities in this role: - 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 Who you are Must have: - 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 — you need to understand what the data means, not just move it. Credit risk, finance, or regulatory reporting background is expected Nice to have: - Snowflake Dynamic Tables — knows when to use them vs Tasks vs stored procedures - Streams and Tasks for incremental processing patterns - Snowpipe or Snowpipe Streaming - DataStage — for working alongside legacy pipelines - Streamlit in Snowflake - SAFe or scaled agile experience Working with AI: - Actively uses AI coding tools as part of daily work - Understands how LLMs work well enough to use them effectively - Keeps up with the AI tooling market Our stack: Snowflake, Snowpark (Python/Scala), Dynamic Tables, Airflow, DataStage (legacy interop), Streamlit in Snowflake, Bitbucket, Jenkins, IntelliJ/VS Code, GitHub Copilot, Gemini Plugin, Cortex and more. What we offer - Hybrid working model - A culture that fosters performance and growth in one of the largest Nordic banks Next steps Submit your application no later than 13/08/2026. Recruitment process: 1. Preliminary CV selection 2. Phone conversation with the recruiter 3. Online interview with the hiring leader 4. Background check
Job Details
Responsibilities
- Design and build transformation jobs in Snowflake using SQL stored procedures, Snowpark, and Dynamic Tables
- Perform requirement analysis to understand business and data context
- Translate business and analytical requirements into maintainable transformation logic
- Apply Snowflake best practices for clustering, query optimization, and warehouse rightsizing
- Contribute to data modeling decisions including schema design and partitioning strategies
- Manage CI/CD deliverables via Bitbucket and Jenkins
- Write and maintain Airflow DAGs for Snowflake workloads
- Contribute to the data exchange layer using Database Roles and Secure Views
- Perform code reviews and pair with team members to maintain standards
Requirements
- 8+ years in data engineering
- At least 2 years hands-on Snowflake in production
- Strong SQL (window functions, QUALIFY, MERGE, recursive CTEs, FLATTEN/LATERAL, performance tuning)
- Snowpark (Python or Scala)
- Understanding of Snowflake execution model (micro-partitions, clustering, pruning, query plan reading)
- Python or Scala for pipeline logic and tooling
- Airflow (DAGs, sensors, retries)
- Git, Bitbucket, CI/CD experience
- Financial services or banking domain knowledge (Credit risk, finance, or regulatory reporting)
- Active use of AI coding tools
Skills & Technologies
Benefits & Perks
Recruitment Process
- 1Preliminary CV selection
- 2Phone conversation with the recruiter
- 3Online interview with the hiring leader
- 4Background check

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