Senior Data Engineer (Python, dbt, cloud)

Dashboards, products and big business decisions all rest on data that someone built properly. This role is for the engineer who builds it.

Our client is a tech consultancy in [REGION] working with mid-sized and large enterprises. You'll build the pipelines, transformations and platforms that clients rely on every day.

At many consultancies, you get placed with a client and rarely see your colleagues again. This one works the other way. The office is busy most days, and shared lunches, after-work evenings and hackathons keep people connected between assignments.

A typical assignment (give or take)

The challenge: a client's data has outgrown its setup. Pipelines break quietly, nobody trusts the numbers, and the warehouse bill keeps climbing.

Your part: you build reliable pipelines, bring in proper orchestration and testing, model the data in dbt, and make the warehouse fast and affordable again.

Every day in between: Python, dbt, colleagues who like talking shop, and a lunch table that fills up.

What you'll do

• Build the pipelines. Design and run production-grade data pipelines in Python, orchestrated so they recover gracefully when something upstream breaks.

• Shape the data. Use dbt to turn raw data into clean, tested, version-controlled models that analysts and applications can trust.

• Keep it healthy. Put CI/CD, automated testing and data quality monitoring in place, so problems surface before the client's dashboards do.

• Work closely with clients. Turn fuzzy requirements into concrete plans, and manage expectations along the way.

Who are you?

You've probably spent years making messy data behave, and somewhere along the way you realised the most interesting problems sit right where the data meets the business.

You'd probably describe yourself as:

• Seasoned: [6+] years in data engineering, with a solid track record in consulting or client-facing project work.

• A real Python engineer: you write clean, reusable, tested code (pytest, proper dependency management), not just scripts.

• A pipeline builder: you've run orchestration with Airflow, Dagster or Prefect, and set up CI/CD, testing and quality monitoring for data.

• Fluent in dbt: it's part of your stack, and you use it for modular, well-tested transformations.

• At home in the warehouse: solid experience with BigQuery or Snowflake, including performance.

• Comfortable in the room: you can manage stakeholders and keep delivering when requirements shift.

Strong plus (but not requried):

• AI: experience with vector databases (e.g. Pinecone, pgvector), RAG pipelines, AI agents or MLOps on Vertex AI or similar. No experience yet but keen to learn? We'd love that too.

• Large-scale processing and cost control: Spark (PySpark or Scala), and a track record of cutting cloud and warehouse costs.

• Advanced dbt: macros, custom materializations, or the dbt Semantic Layer and MetricFlow.

Language: fluent English. Swedish is not required.

Buzzword bingo: Python, dbt, Airflow, Dagster, Prefect, BigQuery, Snowflake, CI/CD, pytest, Spark, RAG, pgvector, Pinecone, Vertex AI, MetricFlow

What's in it for you

• Range without job-hopping. New clients, industries and data challenges, all with the same employer.

• A consultancy that sticks together. Even when you're placed with a client, you're part of a team that meets up regularly over lunch, after work and at hackathons. You're never just a name on a staffing list.

• Room to grow. Space and resources to learn new tech, sharpen your business sense, and grow beyond your own specialty.

• Flexibility when you need it. Hybrid work in Stockholm, depending on what each client allows.

Let's talk

The client is confidential for now, so the full picture comes in a first, discreet conversation.

No cover letter needed. Send your CV or LinkedIn profile. If there's a project or repo you're proud of, include the link. Everything is handled confidentially.

Please note: the role doesn't include work permit sponsorship, so you'll need to already have the right to work in Sweden.