Senior Data Engineer for AI & Analytics Platforms

Confidential; Stockholms kommun; On-site; SEK 900,000 - 1,300,000; Full time

Job summary

Confidential in Stockholm Sweden seeks a senior data/analytics engineer to bridge software engineering with data science, translating complex client needs into scalable AI-enabled platforms. You will design pipelines, model data, and guide enterprise analytics strategies.

Expect client-facing leadership, hands-on engineering, and collaboration across teams to deliver production-grade solutions for varied clients, from startups to large enterprises.

Qualifications

• 6–7+ years in data/analytics engineering with consulting or client-facing experience.
• Deep expertise in dbt, modular data modeling, and SQL version control.
• Strong Python skills with testing frameworks and clean, reusable code.
• Senior-level knowledge of BigQuery or Snowflake, including architecture and performance tuning.
• Experience with CI/CD for data, automated testing, and data quality monitoring.
• Familiarity with vector databases and RAG pipelines.
• Workflow orchestration using Airflow, Dagster, or Prefect.
• Large-scale data processing with Apache Spark.
• FinOps mindset for optimizing cloud spend.
• Exposure to MLOps platforms (e.g., Vertex AI).
• Experience with semantic layers (dbt Semantic Layer, MetricFlow).

Responsibilities

• Design and optimize modern data pipelines and workflows.
• Bridge software engineering with advanced data science.
• Lead client workshops and guide AI strategy.
• Deliver high-quality, scalable solutions across enterprises and startups.

Skills

Client-facing experience

Data engineering

Analytics engineering

Tools

dbt

SQL

BigQuery

Snowflake

Airflow

Dagster

Prefect

Apache Spark

Vertex AI

dbt Semantic Layer

MetricFlow

Vector databases

Job description

Confidential in Stockholm Sweden seeks a senior data/analytics engineer to bridge software engineering with data science, translating complex client needs into scalable AI-enabled platforms. You will design pipelines, model data, and guide enterprise analytics strategies.

Expect client-facing leadership, hands-on engineering, and collaboration across teams to deliver production-grade solutions for varied clients, from startups to large enterprises.