Senior Data Platform Engineer
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Key skills for this role
About the Role
Design and maintain scalable data platforms, optimize ETL pipelines, ensure data security, and provide technical mentorship in data engineering.
Key Skills for This Role
Full Job Posting
Key Responsibilities
- Design, build, and maintain scalable data platforms to support diverse data workloads.
- Develop and optimize ETL/ELT pipelines to ensure efficient data movement and transformation.
- Collaborate with cross-functional teams to translate business requirements into technical solutions.
- Ensure data platform security, governance, and compliance with relevant regulations.
- Monitor platform performance, troubleshoot issues, and implement improvements.
- Provide technical leadership and mentorship to junior data engineers.
- Stay updated with the latest trends and technologies in data platform engineering.
- - Platform architecture: pipeline design patterns, orchestration strategy, infrastructure-as-code, monitoring stack
- - Enterprise data extraction: SAP FI/CO (budget, actuals, commitments via OData/RFC), Microsoft Graph API (SharePoint, M365), SuccessFactors OData API you own the extraction approach and build the first pipelines yourself
- - Staging layer design: PostgreSQL staging schema, raw-to-clean transformation patterns, refresh strategies
- - Ingestion observability: logging standards, alerting on failures, monitoring dashboards for pipeline health
- - Data quality framework: defining quality checks, deciding what pauses a pipeline vs. what triggers a warning
- - Classification enforcement: working with the Governance Lead to embed automated classification tagging into every pipeline
- - Technical mentorship: setting standards and reviewing work for the Senior Data Platform Engineer
• Required Skills & Technologies
- - 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)
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