Staff Data Engineer Platform & Data
- Posted a month ago
- Be among the first 10 applicants
Job Description
Own the data infrastructure that turns Foundry's operational data — scenarios, runs, BOMs, customer usage — into something Post-Sales, Customer Success, and leadership can actually query and trust.
What You'll Do
What You'll Do
- Design and build the data pipeline that moves operational data out of Foundry's tenant-scoped Postgres schemas into an analytics-friendly warehouse, respecting tenant isolation throughout.
- Build the metrics and reporting layer Post-Sales and Customer Success teams use to track customer health, usage, and adoption.
- Own data quality: schema-drift detection, pipeline monitoring, and making sure a broken upstream change doesn't silently corrupt downstream reports.
- Partner with Foundry Core on billing and metering data — usage-based metrics need to be accurate enough to bill against.
- Establish data modeling conventions for a still-young data platform.
- 5+ years of data engineering experience, including building ELT/ETL pipelines from an operational database into a warehouse.
- Strong SQL and experience with a modern data stack (dbt, Airflow, Fivetran, or equivalent — exact tools flexible).
- Experience with multi-tenant data isolation requirements — this pipeline must never leak one customer's data into another's reporting.
- Comfort partnering directly with business stakeholders to define what the right metric actually means.
- Experience building usage-based billing or metering data pipelines specifically.
- Familiarity with warehouse automation or B2B SaaS customer-success metrics.
More Info
About Company
Skills:
snowflake , Data Quality, Databricks, Emr, Tensorflow, Kafka, Pytorch, Terraform, Apache Spark, Kinesis, Presto, Distributed Systems, AWS, Kubernetes, Cloud Infrastructure, Docker, Security, Airflow, dbt, Flink, real-time streaming architectures, model serving, LLM hosting, Trino, MLOps tools, Ray, Kubeflow, Infrastructure-as-Code, Redpanda, Governance, data platforms, observability