Job Description
The Data Engineering Lead (Power & Performance Analytics) role is a high-impact, hands-on position that builds and maintains essential data systems which provide Power and Performance analytics services to multiple SoC programs. The role focuses on consolidating fragmented engineering data into a centralized, trusted database that enables automation, analytics, and scalable dashboarding across AMD’s PnP organization.
Date Posted: NA
Expiration Date: NA
Job ID: 75572
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Key Responsibilities
- Design, implement, and own a centralized database architecture for Power and Performance data across SoC generations.
- Ingest, normalize, and curate structured and unstructured data from Excel, CSV, PPTs, Confluence, SharePoint, and scripts.
- Define and maintain data schemas, metadata, versioning, and lineage to ensure traceability and consistency.
- Build and manage robust ETL / ELT pipelines with validation, error handling, and refresh cadence.
- Ensure historical tracking, data correctness, and long-term scalability of analytics systems.
- Collaborate with PnP engineers, architects, and program managers to translate engineering metrics into structured datasets.
- Enable downstream analytics and dashboards by delivering clean, reliable, and well-documented data
- Act as a technical owner, making architectural trade-offs and driving best practices across teams.
Basic Requirements
- Ability to design databases and perform data engineering tasks.
- Demonstrate advanced skills in SQL together with expertise in PostgreSQL and MySQL and SQL Server databases.
- Experience in developing and operating data pipelines which handle both structured and semi-structured data types.
- Strong data modeling abilities together with complete knowledge about the entire data lifecycle process.
- Demonstrates the capability to handle disorganized and outdated and unformatted engineering information.
- Ability to solve problems independently while maintaining complete accuracy of data.
Preferred Qualifications
- Demonstrate experience with dashboarding or visualization tools like Power BI and Tableau and Grafana and Looker.
- Expertise in Python which he uses for automation and data ingestion through scripting.
- Experience with both cloud-based and scalable data platforms.
- Demonstrates knowledge of version control methods together with data documentation and reproducibility standards.