The Cloud Economics team powers cost transparency, attribution, and optimization across the organization's cloud footprint. This role engages a Platform Automation & Data Engineer to design, build, and maintain the team's data ingestion and automation platforms, blending data engineering, analytical rigor, observability and telemetry analysis, and cloud value management. The objective is to strengthen the team's cost transparency, attribution, and optimization capabilities so that product, engineering, analyst, and finance stakeholders can make faster, data-driven decisions and trust the data.
Develop and implement tooling and datasets that drive telemetry-driven cost attribution and performance-informed financial modeling, enabling product, engineering, analyst, and finance stakeholders to make faster, data-driven decisions and trust the data.
Build and maintain data pipelines and tooling that provide cost transparency, surface optimization opportunities, and support committed use discount modeling and management.
Must-Have Skills:
Hands-on experience with SQL, Python, Spark, and Scala.
Hands-on experience with workflow orchestration tools such as dbt, Airflow, and Dagster.
Hands-on experience with AWS (Glue, Aurora, Athena), GCP, and Snowflake (AWS is required)
Experience architecting and enhancing the long-term maintainability of end-to-end ELT/ETL data pipelines, including data modeling and schema maturity.
Exposure to CI/CD development processes, Infrastructure as Code (Terraform), and REST API programming.
Strong attention to detail, analytical skill, and comprehensive data auditing and end-to-end validation skills.
Ability to explain technical concepts to non-technical audiences.
Nice-to-Have Skills:
Experience with FinOps teams or cost optimization job duties.
FinOps Practitioner or FinOps Engineer certification.
Experience with Datadog API endpoints.
Experience with Go.
