Job description

As a Data Engineer focused on Capacity Planning, you will bring together infrastructure telemetry, workload demand, capacity commitments, and financial data into trusted datasets and data products. You will build the pipelines and analytical foundations used to understand current utilization, forecast future needs, identify capacity gaps, and improve infrastructure efficiency. You will partner with engineering and business teams to turn complex infrastructure data into actionable insights. 3+ years of experience in Data Engineering, Analytics Engineering, Infrastructure Analytics, or a related field. Strong SQL skills and experience working with large datasets. Experience with Python or another language used for data processing and automation. Experience building data pipelines, data models, and analytical datasets. Understanding of ETL/ELT patterns, data quality, and pipeline reliability. Experience working with cloud billing and usage data from AWS, GCP or Azure Proven ability to build data models that reconcile to a financial source of truth Understanding of AI and ML inference workloads and how model serving drives compute cost Strong analytical and problem-solving skills. Ability to work effectively with both technical and non-technical partners. Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Economics, Finance, or a related quantitative field, or equivalent practical experience. Experience with infrastructure capacity planning, forecasting, or resource-management data. Experience working with GPU, TPU, CPU, storage, or cloud infrastructure. Experience with AWS, GCP, or similar cloud platforms. Understanding of AI/ML infrastructure and accelerator utilization. Experience with infrastructure cost, billing, or utilization datasets. Experience with technologies such as Spark, Trino, Airflow, Kafka, or similar data-platform tools. Experience with Tableau or other visualization platforms. Familiarity with infrastructure economics, cloud commitments, or capacity optimization. Experience partnering with Engineering, Finance, or Procurement on infrastructure planning.

Requirements

Must-haves

3+ years of experience in Data Engineering, Analytics Engineering, Infrastructure Analytics, or a related field.
Strong SQL skills and experience working with large datasets.
Experience with Python or another language used for data processing and automation.
Experience building data pipelines, data models, and analytical datasets.
Understanding of ETL/ELT patterns, data quality, and pipeline reliability.
Experience working with cloud billing and usage data from AWS, GCP or Azure
Proven ability to build data models that reconcile to a financial source of truth
Understanding of AI and ML inference workloads and how model serving drives compute cost
Strong analytical and problem-solving skills.
Ability to work effectively with both technical and non-technical partners.
Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Economics, Finance, or a related quantitative field, or equivalent practical experience.
Experience with infrastructure capacity planning, forecasting, or resource-management data.
Experience working with GPU, TPU, CPU, storage, or cloud infrastructure.
Experience with AWS, GCP, or similar cloud platforms.
Understanding of AI/ML infrastructure and accelerator utilization.
Experience with infrastructure cost, billing, or utilization datasets.
Experience with technologies such as Spark, Trino, Airflow, Kafka, or similar data-platform tools.
Experience with Tableau or other visualization platforms.
Familiarity with infrastructure economics, cloud commitments, or capacity optimization.
Experience partnering with Engineering, Finance, or Procurement on infrastructure planning.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpythonawsgcpazureaiml

Platform: apple
Effort: medium