data engineer (fintech) Salary in Stockholm (2026): Complete Guide

A data engineer (fintech) in Stockholm typically earns $58k–$145k USD base salary in 2026, with the middle of the market landing around $78k–$110k. Senior candidates with strong cloud, streaming, and regulatory data experience can push higher, especially in fintechs competing with banks and payments firms.

Salary by Experience

A few notes on Stockholm pricing:

• •Fintech pays a premium over generic enterprise data engineering.
• •Candidates with AWS, GCP, Snowflake, Databricks, Kafka, dbt, and Terraform usually sit above median.
• •Total compensation can be materially higher if equity is meaningful, but many Stockholm fintechs keep equity modest and lean on cash base plus bonus.

What Affects Your Salary

• •

  Fintech domain depth

  • •If you’ve built data platforms for payments, lending, fraud, AML, or risk reporting, expect a premium.
  • •Stockholm has a strong finance and fintech presence, so employers pay more for people who understand regulated data flows.
• •

  Cloud and streaming specialization

  • •Batch-only ETL is commoditized.
  • •Engineers who can design event-driven pipelines with Kafka/Kinesis/PubSub, manage lakehouse stacks, and own infrastructure as code are priced higher.
• •

  Regulatory exposure

  • •Experience with GDPR, auditability, lineage, retention policies, and controls around PII increases your value.
  • •In fintech, “can ship data” matters less than “can ship data without creating compliance debt.”
• •

  Company type

  • •Large banks usually pay more predictably but with tighter bands.
  • •Fintech scaleups often pay aggressively for scarce talent, especially if they’re hiring for platform reliability or ML-ready data foundations.
• •

  Remote vs onsite

  • •Fully remote roles that hire across Sweden may compress salaries slightly.
  • •Hybrid roles in central Stockholm often pay better if they require ownership of critical systems and frequent collaboration with product, risk, or compliance teams.

How to Negotiate

• •

  Anchor on business-critical outcomes

  • •Don’t lead with “I know Airflow.”
  • •Lead with outcomes: reduced pipeline failures, faster fraud feature delivery, lower warehouse spend, better audit readiness.
• •

  Price the regulatory burden

  • •If you’ve worked on KYC/AML pipelines, customer identity graphs, transaction monitoring feeds, or reporting to regulators, make that explicit.
  • •That work is harder to replace than generic analytics engineering.
• •

  Separate base from total comp

  • •Ask for the full package: base salary, bonus target, pension contributions, equity vesting terms, and any sign-on bonus.
  • •In Stockholm fintechs, pension contributions and bonus structure can move the real value by a noticeable amount.
• •

  Use market scarcity correctly

  • •If you bring rare skills like real-time fraud detection pipelines or feature store experience for ML teams, say so directly.
  • •The strongest negotiation position is not “I want more,” it’s “this role needs someone who can own X end-to-end.”

Comparable Roles

• •

  Analytics Engineer — $65k–$105k USD

  • •Usually slightly below senior data engineer unless the role is close to platform ownership.
• •

  Data Platform Engineer — $85k–$130k USD

  • •Often overlaps with senior/principal data engineering and can pay more if it includes infrastructure responsibility.
• •

  ML Data Engineer — $90k–$140k USD

  • •Tends to run higher because it supports model training pipelines, feature stores, and production ML systems.
• •

  Backend Engineer (Payments/Fraud) — $80k–$135k USD

  • •Comparable when the role includes heavy event processing and transactional systems work.
• •

  BI Engineer / Reporting Engineer — $60k–$95k USD

  • •Usually below core fintech data engineering unless tied to regulatory reporting or mission-critical dashboards.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.