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Compensation Data Engineer - Hewlett Packard Enterprise HPE
Data Engineer
Design, build, and maintain scalable data pipelines and warehouses for compensation analytics, leveraging Python, SQL, cloud services, and big‑data technologies to deliver reliable, high‑performance data solutions.
About the role
Key Responsibilities
- Develop, test, and deploy end‑to‑end ETL pipelines that ingest, transform, and load compensation data from multiple sources.
- Design and optimize data warehouse schemas to support reporting, analytics, and machine‑learning use cases.
- Implement data quality checks, monitoring, and alerting to ensure accuracy and reliability of compensation datasets.
- Collaborate with compensation analysts, HR systems owners, and engineering teams to define data requirements and deliver timely solutions.
- Leverage cloud platforms (AWS/Azure) and big‑data frameworks (Spark, Hadoop) to scale processing workloads.
- Document data models, pipeline architecture, and operational procedures for knowledge sharing.
Requirements
- 3+ years of experience building data pipelines and warehouses using Python, SQL, and ETL tools.
- Strong understanding of relational and columnar databases, data modeling, and performance tuning.
- Hands‑on experience with cloud services (e.g., AWS Redshift, Azure Synapse) and big‑data processing frameworks such as Spark.
- Proficiency in implementing data quality, monitoring, and CI/CD practices for data workflows.
- Excellent problem‑solving skills and ability to work cross‑functionally with business stakeholders.