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Mid-level metro data engineering role with common skills increases competition, moderated by niche HR/PII requirements.
Requires HR domain knowledge and sensitive-data handling, limiting cross-industry transferability.
Mandatory 5+ years, specific cloud/dbt/warehouse stack, and sensitive-PII security experience create strict filters.
Design, scale, and optimize the global HR data warehouse architecture to support enterprise BI, workforce analytics, AI, and machine learning workloads.
Partner with AI, Data Science, and HR tech teams to develop clean training datasets, feature-ready data models, and governed data pipelines for predictive analytics and LLM/chatbot use cases.
Build and maintain dashboards, curated datasets, and self-service analytics products for HR and business stakeholders; mentor junior engineers on technical best practices.
5+ years in Analytics Engineering, Data Engineering, or Business Intelligence Engineering, preferably with People Analytics or sensitive HR data.
Expertise in cloud data warehouses such as Google BigQuery or Snowflake with advanced SQL and data modeling skills.
Experience with dbt, git, automated testing, and orchestration tools like Airflow, Prefect, or Dagster.
Strong knowledge of data security for sensitive PII including encryption, masking, tokenization, and governance frameworks.
Technical architect comfortable bridging complex data engineering with AI/ML and advanced analytics in a global HR context.
Experienced in enterprise-scale, privacy-aware data modeling and governance specifically for HR and employee data.
Able to manage multi-stakeholder projects with focus on data quality, security, documentation, and business-technical communication.