Job VC
Senior Data Platform Engineer
Technologies
Description
We build and operate AI-enabled systems that create measurable operational impact across the organization. Our primary output is working software in production.
Anchors the data platform architecture and owns enterprise data extraction end-to-end.
Ideal Candidate
Has designed and built a data platform from scratch in an enterprise environment — not inherited a running system, built one. Has pulled data from SAP or comparable ERP into a warehouse and survived upstream failures without silent data loss. Sets standards that other engineers can follow without three clarification meetings. Can make architecture calls quickly and correct them later. Has strong opinions about pipeline observability and data quality.
Skills Required
SAP data extraction: OData services, RFC/BAPI, SAP Data Services, or CDS views — hands-on experience pulling data from SAP FI/CO modules
Python — async data pipelines, background jobs, scheduled tasks
PostgreSQL — schema design, migrations (Alembic), query optimization, partitioning
Azure Data Lake Storage + Synapse Analytics
Microsoft Graph API — SharePoint, M365, organizational data
Apache Airflow or Azure Data Factory
dbt or equivalent for transformation and quality testing
Redis — queue management, TTL, cache invalidation
Observability — structured logging, Azure Monitor or Prometheus/Grafana
Docker — containerized pipeline jobs
Infrastructure-as-code (Terraform or equivalent)
Anchors the data platform architecture and owns enterprise data extraction end-to-end.
Ideal Candidate
Has designed and built a data platform from scratch in an enterprise environment — not inherited a running system, built one. Has pulled data from SAP or comparable ERP into a warehouse and survived upstream failures without silent data loss. Sets standards that other engineers can follow without three clarification meetings. Can make architecture calls quickly and correct them later. Has strong opinions about pipeline observability and data quality.
Skills Required
SAP data extraction: OData services, RFC/BAPI, SAP Data Services, or CDS views — hands-on experience pulling data from SAP FI/CO modules
Python — async data pipelines, background jobs, scheduled tasks
PostgreSQL — schema design, migrations (Alembic), query optimization, partitioning
Azure Data Lake Storage + Synapse Analytics
Microsoft Graph API — SharePoint, M365, organizational data
Apache Airflow or Azure Data Factory
dbt or equivalent for transformation and quality testing
Redis — queue management, TTL, cache invalidation
Observability — structured logging, Azure Monitor or Prometheus/Grafana
Docker — containerized pipeline jobs
Infrastructure-as-code (Terraform or equivalent)