Data Engineer (Senior/Staff)

Demaai AB; Stockholms kommun; Hybrid; SEK 600,000 - 800,000; Full time

Job summary

Demaai AB is seeking a Senior or Staff Data Engineer to help design, build, and maintain systems for their commerce analytics platform. You will work on data modeling, streaming pipelines, and batch processing, utilizing emergent AI-assisted development processes.

The ideal candidate should have hands-on experience with big data fundamentals, stream and batch processing tools, and a solid understanding of cloud infrastructure. Both remote and hybrid working arrangements are available for this role.

Qualifications

• Proven hands-on experience with big data fundamentals.
• Experience in stream processing, particularly with Apache Flink.
• Familiarity with batch processing tools like Prefect or Airflow.

Responsibilities

• Design and evolve data models and contracts.
• Implement streaming pipelines and manage their health.
• Handle batch processing and orchestration using Prefect.

Skills

Big data fundamentals

Apache Flink

Batch processing

Lakehouse formats

Data modeling

Cloud infrastructure

Engineering habits

Tools

Terraform

AWS

GCP

Apache Kafka

Prefect

Job description

As a Data Engineer (Senior / Staff) at Dema, you will help design, build, and maintain the systems that power our commerce analytics platform. Your work will contribute to reliable data flows, scalable infrastructure, and product features that help our customers turn complex data into clear insights.

We believe software development is being fundamentally reshaped by AI. We actively adopt modern AI-assisted development workflows and expect engineers to explore how these tools can improve both speed and quality.

Our engineers use AI tools to prototype ideas faster, accelerate debugging and refactoring, and automate repetitive tasks. This allows us to spend more time solving real problems, improving architecture, and building great products (Claude Code, Cursor, and similar)

What you will actually work on

• Designing and evolving the data model across layers, and the contracts (Avro) that hold it all together
• Streaming pipelines on Apache Flink and Kafka — topology design, state management, checkpointing, and the operational realities of keeping them healthy
• Our Iceberg lake and ClickHouse warehouse — partitioning, compaction, retention, schema evolution, zero-downtime migrations, and query performance
• Batch processing and orchestration with Prefect — flow design and performance tuning at the task level
• The conceptual layer customers see: metrics, dimensions, marts, and the modeling decisions that make them trustworthy
• Infrastructure as code (Terraform on AWS and GCP, Kubernetes, Helm) for the systems you own
• Observability — OpenTelemetry traces, custom metrics, and the kind of instrumentation that lets you debug production from a dashboard instead of a hunch

What we are looking for

We don’t have a rigid must‑have list. We’d rather meet candidates who have proven, hands‑on experience with a meaningful subset of the areas below, along with a strong grasp of the underlying concepts:

• Big data fundamentals — partitioning, shuffles, skew, late-arriving data, exactly‑once semantics, idempotency, and understanding why your join just did something terrible
• Stream processing — Apache Flink especially, but Spark Structured Streaming, Kafka Streams, and Beam are all fair game
• Batch processing and orchestration — Prefect, Dagster, Airflow; ETL/ELT pipeline design and dependency management
• Lakehouse formats — Iceberg, Paimon, Delta, Hudi; metadata management, compaction, and schema evolution
• Analytical warehouses — ClickHouse, BigQuery, Snowflake, Redshift, DuckDB; and the trade‑offs between them
• Data modeling concepts — medallion and mart architectures, dimensional modeling, metrics-versus-dimensions thinking, slowly changing dimensions, and understanding the difference between a fact and an aggregate
• Cloud infrastructure — AWS, GCP, or Azure, with infrastructure‑as‑code tools such as Terraform; comfortable owning what you ship
• Engineering habits — instrumentation, testing data pipelines, schema governance, and treating data contracts as APIs

We’re language‑agnostic on the candidate side. Our stack happens to be Python, Java, SQL, and TypeScript, but if you understand the concepts deeply, you’ll pick up whatever’s missing.

About the role

• Senior or Staff level, we’ll match the level to you, not the other way around
• Remote or hybrid, both work
• High ownership and a short distance from idea to production