Data Engineer (Senior/Staff)

DEMA; Stockholms kommun; Hybrid; SEK 700,000 - 1,100,000; Full time

Job summary

Dema is seeking a Senior/Staff Data Engineer to design, build, and maintain systems powering our commerce analytics platform. You will influence data flows, scalable infrastructure, and product features that turn complex data into clear insights.

We embrace AI-assisted development and expect engineers to explore tools that speed up delivery while improving quality. Remote or hybrid work is supported, with high ownership and fast production exposure.

Qualifications

• Hands-on experience with large-scale data platforms and analytics.
• Proven ability to design and optimize data models and contracts (Avro).
• Experience in streaming pipelines, batch orchestration, and lakehouse storage.
• Familiarity with Iceberg, ClickHouse, and modern analytical warehouses is a plus.
• Strong grasp of data contracts, schema evolution, and observability.

Responsibilities

• Designing and evolving data models across layers and contracts (Avro).
• Build and maintain streaming pipelines with Flink and Kafka, with health checks.
• Manage Iceberg lake and ClickHouse warehouse for partitioning and performance.
• Batch processing and orchestration with Prefect; flow design and tuning.
• Model metrics, dimensions, marts; ensure trustworthy analytics.
• Implement IaC using Terraform, Kubernetes, and Helm; manage cloud infra.
• Enhance observability with OpenTelemetry and custom metrics.

Skills

Big data fundamentals

Stream processing

Batch processing

Lakehouse formats

Analytical warehouses

Data modeling concepts

Cloud infrastructure

Engineering habits

Tools

Apache Flink

Kafka

Iceberg

Kubernetes

Terraform

Airflow

Cloud platforms (AWS/GCP/Azure)

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