Data Engineer (Senior/Staff)

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