Staff Data Engineer

8+ years of hands-on experience in data engineering, with a proven track record of delivering production-grade data solutions.
Expertise in distributed data processing frameworks such as Apache Spark, Flink, or equivalent.
Strong SQL and data modeling skills, with experience in columnar databases like BigQuery, Snowflake, or Redshift.
Deep knowledge of cloud platforms (preferably GCP and AWS), and experience with cloud-native storage and processing tools.
Experience with modern data formats (Parquet, ORC, Iceberg) and orchestration tools (Airflow, Dagster, dbt, etc.).
Solid programming skills in Python, Java, or Scala.

Design and build scalable, high-performance data pipelines using modern technologies (e.g., Spark, BigQuery, Kafka, Airflow, Iceberg).
Partner closely with Product, Engineering, and FinOps teams to define data models and infrastructure to support cost analytics, forecasting, and optimization.
Lead architectural decisions and help establish best practices in data quality, governance, and observability.
Collaborate with cross-functional teams to integrate new data sources and ensure data consistency across cloud providers.
Identify bottlenecks in pipeline performance and propose innovative solutions to improve throughput and reliability.
Mentor other engineers and help raise the data engineering bar across the organization.
Contribute to overall platform performance, availability, and reliability from a data infrastructure lens.

Experience with Kubernetes and containerized deployments is a plus.
Prior experience in FinOps, cloud cost optimization, or billing systems is highly desirable.
Strong communication and collaboration skills – you can influence architecture, design, and roadmap across teams.