Design and build cloud-based data pipelines for ingesting, transforming, and storing large volumes of data.
Implement scalable, fault-tolerant systems using modern frameworks such as Spark, Temporal, Airflow or Dagster.
Develop APIs and services that expose reliable data access, transformation, and orchestration capabilities.
Contribute to geospatial data processing workflows using tools such as Apache Sedona and native Iceberg geospatial functionality.
Assist in developing and maintaining shared platform tooling and libraries that improve productivity and promote self-service capabilities across engineering and analytics teams.
Monitor, tune, and troubleshoot the performance, cost, and reliability of data infrastructure components.
Collaborate closely with cross-functional teams to ensure platform systems integrate smoothly with analytics, visualization, and downstream applications.

3-5 years of experience in data engineering, ideally working with large-scale or geospatial systems.
Proficiency in Python and SQL (Spark SQL preferred); experience with distributed data processing frameworks such as Spark, Flink, or Beam.
Familiarity with orchestration frameworks (e.g., Airflow, Temporal, Dagster) and workflow automation principles.
Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
Exposure to batch and streaming architectures and understanding of modern data lake or lakehouse patterns (e.g., Iceberg, Delta, Hudi).
Knowledge of geospatial data formats, processing libraries, and coordinate systems is a plus.
Strong problem-solving skills and an interest in building reliable, scalable, and observable data systems.

Competitive salary
Comprehensive health, dental, and vision insurance plans
Flexible remote work environment
Additional benefits like flexible hours, work travel opportunities, competitive vacation time and parental leave

Intermediate Backend Engineer

Key skills