As aData Engineer (Senior / Staff) atDema, 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

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Data Engineer (Senior/Staff)